From 657c2baf727bfa35661693708b4bb1342b77f149 Mon Sep 17 00:00:00 2001
From: Ben Anderson <dataknut@icloud.com>
Date: Wed, 11 Nov 2020 18:50:18 +0000
Subject: [PATCH] restructured and updated, saves out final EPC data with
 linked geocodes

---
 EPCsAndCarbon/epcChecks.Rmd |  325 +++--
 docs/epcChecks.html         | 2421 +++++++++++++++++++----------------
 2 files changed, 1540 insertions(+), 1206 deletions(-)

diff --git a/EPCsAndCarbon/epcChecks.Rmd b/EPCsAndCarbon/epcChecks.Rmd
index 8bd0a3a..e8a1b9c 100644
--- a/EPCsAndCarbon/epcChecks.Rmd
+++ b/EPCsAndCarbon/epcChecks.Rmd
@@ -55,14 +55,15 @@ We have to assume the data we have is the _current state of play_ for these dwel
 
 # Data loading
 
+## EPCs
 Load the data for the area of interest - in this case the City of Southampton.
 
 ```{r, loadSoton}
 df <- path.expand("~/data/EW_epc/domestic-E06000045-Southampton/certificates.csv")
-allEPCs_DT <- data.table::fread(df)
+sotonEPCsDT <- data.table::fread(df)
 ```
 
-The EPC data file has `r nrow(allEPCs_DT)` records for Southampton and `r ncol(allEPCs_DT)` variables. We're not interested in all of these, we want:
+The EPC data file has `r nrow(sotonEPCsDT)` records for Southampton and `r ncol(sotonEPCsDT)` variables. We're not interested in all of these, we want:
 
  * PROPERTY_TYPE: Describes the type of property such as House, Flat, Maisonette etc. This is the type differentiator for dwellings;
  * BUILT_FORM: The building type of the Property e.g. Detached, Semi-Detached, Terrace etc. Together with the Property Type, the Build Form produces a structured description of the property;
@@ -82,15 +83,15 @@ The EPC data file has `r nrow(allEPCs_DT)` records for Southampton and `r ncol(a
   
 These may indicate 'non-grid' energy inputs.
 
-## Select most recent records
+### Select most recent records
 
 If an EPC has been updated or refreshed, the EPC dataset will hold multiple EPC records for that property (see Table \@ref(tab:plotAllRecords)).
 
 ```{r, plotAllRecords, fig.cap="All records: Inspection date"}
-ggplot2::ggplot(allEPCs_DT, aes(x = INSPECTION_DATE)) +
+ggplot2::ggplot(sotonEPCsDT, aes(x = INSPECTION_DATE)) +
   geom_histogram()
 
-t <- allEPCs_DT[, .(nRecords = .N,
+t <- sotonEPCsDT[, .(nRecords = .N,
                     firstDate = min(INSPECTION_DATE),
                     lastDate = max(INSPECTION_DATE)), keyby = .(BUILDING_REFERENCE_NUMBER)]
 
@@ -100,7 +101,7 @@ Figure \@ref(fig:plotAllRecords) shows the inspection date of all EPC records. W
 
 ```{r, checkData}
 # select just these vars
-dt <- allEPCs_DT[, .(BUILDING_REFERENCE_NUMBER, LMK_KEY, LODGEMENT_DATE,INSPECTION_DATE, PROPERTY_TYPE, BUILT_FORM,
+dt <- sotonEPCsDT[, .(BUILDING_REFERENCE_NUMBER, LMK_KEY, LODGEMENT_DATE,INSPECTION_DATE, PROPERTY_TYPE, BUILT_FORM,
                 ENVIRONMENT_IMPACT_CURRENT, ENERGY_CONSUMPTION_CURRENT, CO2_EMISSIONS_CURRENT, TENURE,
                 PHOTO_SUPPLY, WIND_TURBINE_COUNT, TOTAL_FLOOR_AREA, 
                 POSTCODE, LOCAL_AUTHORITY_LABEL)]
@@ -129,7 +130,7 @@ ggplot2::ggplot(sotonUniqueEPCsDT, aes(x = INSPECTION_DATE)) +
   geom_histogram()
 ```
 
-## Descriptives
+### Descriptives
 
 Now check the distributions of the retained variables.
 
@@ -145,9 +146,127 @@ As we can see that we have `r uniqueN(dt$BUILDING_REFERENCE_NUMBER)` unique prop
 
 This is not surprising since the kWh/y and TCO2/y values are estimated using a model but before we go any further we'd better check if these are significant in number.
 
-# EPC data checks
+## Postcode data
 
-## Check ENERGY_CONSUMPTION_CURRENT
+Load postcodes for Southampton (contains other geo-codes for linkage). 
+
+Source: https://geoportal.statistics.gov.uk/datasets/national-statistics-postcode-lookup-august-2020 
+
+```{r, loadPostcodes}
+
+# Load the postcode based MSOA codes
+soPostcodesDT <- data.table::fread(path.expand("~/data/UK_postcodes/NSPL_AUG_2020_UK/Data/multi_csv/NSPL_AUG_2020_UK_SO.csv"))
+
+#soPostcodesDT <- soPostcodesDT[is.na(doterm)] # keep current
+# keep all as some of the defunct ones will be in the EPC data (!)
+
+sotonPostcodesDT <- soPostcodesDT[laua == "E06000045"] # keep Southampton City
+
+sotonPostcodesReducedDT <- sotonPostcodesDT[, .(pcd, pcd2, pcds, laua, msoa11, lsoa11)]
+
+message("Example data")
+head(sotonPostcodesReducedDT)
+```
+
+## BEIS data
+
+Load BEIS energy demand data. 
+
+Source: https://geoportal.statistics.gov.uk/datasets/national-statistics-postcode-lookup-august-2020 
+
+```{r, loadBEIS}
+beisElecDT <- data.table::fread("~/data/beis/MSOA_DOM_ELEC_csv/MSOA_ELEC_2018.csv")
+sotonElecDT <- beisElecDT[LAName %like% "Southampton", .(nElecMeters = METERS,  
+                                                         beisElecMWh = KWH/1000, 
+                                                         MSOACode, LAName)
+                          ]
+
+
+beisGasDT <- data.table::fread("~/data/beis/MSOA_DOM_GAS_csv/MSOA_GAS_2018.csv")
+sotonGasDT <- beisGasDT[LAName %like% "Southampton", .(nGasMeters = METERS,  
+                                                         beisGasMWh = KWH/1000, 
+                                                         MSOACode)]
+
+setkey(sotonElecDT, MSOACode)
+setkey(sotonGasDT, MSOACode)
+sotonEnergyDT <- sotonGasDT[sotonElecDT]
+sotonEnergyDT[, beisEnergyMWh := beisElecMWh + beisGasMWh]
+#head(sotonEnergyDT)
+message("Example data (retained variables)")
+head(sotonEnergyDT)
+```
+
+## Census data
+
+Load Census 2011 tenure data.
+
+Source: https://www.nomisweb.co.uk/census/2011/ks402ew
+
+```{r, loadCensus}
+# census tenure ----
+dt <- data.table::fread(path.expand("~/data/census2011/2011_MSOA_householdTenure_Soton.csv"))
+
+dt[, census2011_socialRent := `Tenure: Social rented; measures: Value`]
+dt[, census2011_privateRent := `Tenure: Private rented; measures: Value`]
+dt[, census2011_ownerOccupy := `Tenure: Owned; measures: Value`]
+dt[, census2011_other := `Tenure: Living rent free; measures: Value`]
+dt[, MSOACode := `geography code`]
+
+dt[, hhCheck := census2011_socialRent + census2011_privateRent + census2011_ownerOccupy + census2011_other]
+dt[, nHHs_tenure := `Tenure: All households; measures: Value`]
+
+dt[, socRent_pc := 100*(census2011_socialRent/nHHs_tenure)]
+dt[, privRent_pc := 100*(census2011_privateRent/nHHs_tenure)]
+dt[, ownerOcc_pc := 100*(census2011_ownerOccupy/nHHs_tenure)]
+
+tenureDT <- dt[, .(MSOACode, nHHs_tenure, socRent_pc, privRent_pc, ownerOcc_pc)]
+message("Example data (retained variables)")
+head(tenureDT) # all tenure data
+```
+
+## Deprivation data
+
+Load IMD data. 
+
+Source: https://www.nomisweb.co.uk/census/2011/qs119ew
+
+```{r, loadDeprivation}
+
+
+# add the  deprivation data by MSOA
+dt <- data.table::fread(path.expand("~/data/census2011/2011_MSOA_deprivation.csv"))
+dt[, nHHs_deprivation := `Household Deprivation: All categories: Classification of household deprivation; measures: Value`]
+dt[, MSOACode := `geography code`]
+
+#sotonDep_DT[, .(nHouseholds = sum(totalHouseholds)), keyby = .(LAName)]
+
+dt[, dep0_pc := 100*(`Household Deprivation: Household is not deprived in any dimension; measures: Value`/nHHs_deprivation)]
+dt[, dep1_pc := 100*(`Household Deprivation: Household is deprived in 1 dimension; measures: Value`/nHHs_deprivation)]
+dt[, dep2_pc := 100*(`Household Deprivation: Household is deprived in 2 dimensions; measures: Value`/nHHs_deprivation)]
+dt[, dep3_pc := 100*(`Household Deprivation: Household is deprived in 3 dimensions; measures: Value`/nHHs_deprivation)]
+dt[, dep4_pc := 100*(`Household Deprivation: Household is deprived in 4 dimensions; measures: Value`/nHHs_deprivation)]
+
+deprivationDT <- dt[, .(MSOACode, nHHs_deprivation, dep0_pc, dep1_pc, dep2_pc, dep3_pc, dep4_pc)]
+# sneak the LA name in there too
+dt <- sotonEnergyDT[,.(MSOACode,LAName)]
+setkey(dt, MSOACode)
+setkey(deprivationDT, MSOACode)
+
+sotonDeprivationDT <- deprivationDT[dt] # has the side effect of dropping non-Soton MSOAs
+
+message("Example data (retained variables)")
+head(sotonDeprivationDT)
+
+# merge with census for future use
+setkey(sotonDeprivationDT, MSOACode)
+setkey(tenureDT, MSOACode)
+sotonCensus2011_DT <- tenureDT[sotonDeprivationDT] # only Soton MSOAs
+
+```
+
+# Data checks
+
+## EPC: Check ENERGY_CONSUMPTION_CURRENT
 
 We recode the current energy consumption into categories for comparison with other low values and the presence of wind turbines/PV. We use -ve, 0 and 1 kWh as the thresholds of interest.
 
@@ -198,7 +317,7 @@ ggplot2::ggplot(sotonUniqueEPCsDT[TENURE != "NO DATA!" &
   theme(legend.position = "bottom")
 ```
 
-## Check CO2_EMISSIONS_CURRENT
+## EPC: Check CO2_EMISSIONS_CURRENT
 
 Next we do the same for current emissions. Repeat the coding for total floor area using 0 and 1 TCO2/y as the threshold of interest.
 
@@ -231,12 +350,12 @@ kableExtra::kable(round(100*(prop.table(table(sotonUniqueEPCsDT$emissionsFlag,
 
 There are `r nZeroEmissions` properties with 0 or negative emissions. It looks like they are also the properties with -ve kWh as we might expect. So we can safely ignore them.
 
-## Check ENVIRONMENT_IMPACT_CURRENT
+## EPC: Check ENVIRONMENT_IMPACT_CURRENT
 
 `Environmental impact` should decrease as emissions increase.
 
 ```{r, checkImpact, fig.cap="Histogram of ENVIRONMENT_IMPACT_CURRENT"}
-ggplot2::ggplot(allEPCs_DT, aes(x = ENVIRONMENT_IMPACT_CURRENT)) +
+ggplot2::ggplot(sotonEPCsDT, aes(x = ENVIRONMENT_IMPACT_CURRENT)) +
   geom_histogram()
 ```
 
@@ -244,7 +363,7 @@ So what is the relationship between ENVIRONMENT_IMPACT_CURRENT and CO2_EMISSIONS
 
 ```{r, checkEmissionsImpact, fig.cap="Plot of ENVIRONMENT_IMPACT_CURRENT vs CO2_EMISSIONS_CURRENT"}
 
-ggplot2::ggplot(allEPCs_DT, aes(x = CO2_EMISSIONS_CURRENT, 
+ggplot2::ggplot(sotonEPCsDT, aes(x = CO2_EMISSIONS_CURRENT, 
                            y = ENVIRONMENT_IMPACT_CURRENT,
                            colour = TENURE)) +
   geom_point() +
@@ -252,7 +371,7 @@ ggplot2::ggplot(allEPCs_DT, aes(x = CO2_EMISSIONS_CURRENT,
   theme(legend.position = "bottom")
 ```
 
-## Check TOTAL_FLOOR_AREA
+## EPC: Check TOTAL_FLOOR_AREA
 
 Repeat the coding for total floor area using 5 m2 as the threshold of interest.
 
@@ -264,8 +383,8 @@ nZeroFloorArea <- nrow(sotonUniqueEPCsDT[TOTAL_FLOOR_AREA < 0])
 
 sotonUniqueEPCsDT[, floorFlag := ifelse(TOTAL_FLOOR_AREA == 0, "0 m2", NA)]
 sotonUniqueEPCsDT[, floorFlag := ifelse(TOTAL_FLOOR_AREA > 0 & 
-                                     TOTAL_FLOOR_AREA <= 10, "0-5 m2", floorFlag)]
-sotonUniqueEPCsDT[, floorFlag := ifelse(TOTAL_FLOOR_AREA > 10, "5+ m2", floorFlag)]
+                                     TOTAL_FLOOR_AREA <= 5, "0-5 m2", floorFlag)]
+sotonUniqueEPCsDT[, floorFlag := ifelse(TOTAL_FLOOR_AREA > 5, "5+ m2", floorFlag)]
 
 t <- with(sotonUniqueEPCsDT, table(floorFlag, consFlag))
 
@@ -287,101 +406,23 @@ Table \@ref(tab:checkEmissions) shows that the properties with floor area of < 1
 
 The scale of the x axis also suggests a few very large properties.
 
-## Data summary
-
-We have identified some issues with a small number of the properties in the EPC dataset. These are not unexpected given that much of the estimates rely on partial or presumed data. Data entry errors are also quite likely. As a result we exclude:
-
- * any property where ENERGY_CONSUMPTION_CURRENT <= 0
- * any property where TOTAL_FLOOR_AREA <= 5
- * any property where CO2_EMISSIONS_CURRENT <= 0
-
-```{r, finalData}
-finalEPCDT <- sotonUniqueEPCsDT[ENERGY_CONSUMPTION_CURRENT > 0 &
-                      TOTAL_FLOOR_AREA > 5 &
-                      CO2_EMISSIONS_CURRENT > 0]
-
-skimr::skim(finalEPCDT)
-```
-
-This leaves us with a total of `r prettyNum(nrow(finalEPCDT), big.mark = ",")` properties.
-
-```{r, saveFinalData}
-of <- path.expand("~/data/EW_epc/domestic-E06000045-Southampton/finalClean.csv")
-data.table::fwrite(finalEPCDT, file = of)
-
-message("Gziping ", of)
-# Gzip it
-# in case it fails (it will on windows - you will be left with a .csv file)
-try(system( paste0("gzip -f '", of,"'"))) # include ' or it breaks on spaces
-message("Gzipped ", of)
-
-```
-
-
-# Check 'missing' EPC rates
+## EPC: Check 'missing' EPC rates
 
 We know that we do not have EPC records for every dwelling. But how many are we missing? We will check this at MSOA level as it allows us to link to other MSOA level datasets that tell us how many households, dwellings or energy meters to expect. Arguably it would be better to do this at LSOA level but...
 
-First we'll use the BEIS 2018 MSOA level annual electricity data to estimate the number of meters (not properties) - some addresses can have 2 meters (e.g. standard & economy 7). This is more useful than the number of gas meters since not all dwellings have mains gas but all have an electricity meter.
+First we'll use the BEIS 2018 MSOA level annual electricity data to estimate the number of meters (not properties) - some addresses can have 2 meters (e.g. standard & economy 7). However this is more useful than the number of gas meters since not all dwellings have mains gas but all (should?) have an electricity meter.
 
-```{r, checkBEIS}
-beisElecDT <- data.table::fread("~/data/beis/MSOA_DOM_ELEC_csv/MSOA_ELEC_2018.csv")
-sotonElecDT <- beisElecDT[LAName %like% "Southampton", .(nElecMeters = METERS,  
-                                                         beisElecMWh = KWH/1000, 
-                                                         MSOACode, LAName)
-                          ]
-
-
-beisGasDT <- data.table::fread("~/data/beis/MSOA_DOM_GAS_csv/MSOA_GAS_2018.csv")
-sotonGasDT <- beisGasDT[LAName %like% "Southampton", .(nGasMeters = METERS,  
-                                                         beisGasMWh = KWH/1000, 
-                                                         MSOACode)]
-
-setkey(sotonElecDT, MSOACode)
-setkey(sotonGasDT, MSOACode)
-sotonEnergyDT <- sotonGasDT[sotonElecDT]
-sotonEnergyDT[, beisEnergyMWh := beisElecMWh + beisGasMWh]
-#head(sotonEnergyDT)
+```{r, checkBEISmeters}
+sotonEnergyDT[, .(nElecMeters = sum(nElecMeters),
+                  nGasMeters = sum(nGasMeters)), keyby = .(LAName)]
 ```
 
 Next we'll check for the number of households reported by the 2011 Census.
 
-> would be better to use dwellings but this gives us tenure
+> would be better to use dwellings but this gives us tenure as well
 
 ```{r, checkCensus}
 #censusDT <- data.table::fread(path.expand("~/data/"))
-# IMD ----
-deprivationDT <- data.table::fread(path.expand("~/data/census2011/2011_MSOA_deprivation.csv"))
-deprivationDT[, totalHouseholds := `Household Deprivation: All categories: Classification of household deprivation; measures: Value`]
-deprivationDT[, MSOACode := `geography code`]
-setkey(deprivationDT, MSOACode)
-setkey(sotonElecDT, MSOACode)
-# link LA name from Soton elec for now
-sotonDep_DT <- deprivationDT[sotonElecDT[, .(MSOACode, LAName)]]
-sotonDep_DT[, nHHs_deprivation := `Household Deprivation: All categories: Classification of household deprivation; measures: Value`]
-
-#sotonDep_DT[, .(nHouseholds = sum(totalHouseholds)), keyby = .(LAName)]
-
-# census tenure ----
-sotonTenureDT <- data.table::fread(path.expand("~/data/census2011/2011_MSOA_householdTenure_Soton.csv"))
-
-sotonTenureDT[, census2011_socialRent := `Tenure: Social rented; measures: Value`]
-sotonTenureDT[, census2011_privateRent := `Tenure: Private rented; measures: Value`]
-sotonTenureDT[, census2011_ownerOccupy := `Tenure: Owned; measures: Value`]
-sotonTenureDT[, census2011_other := `Tenure: Living rent free; measures: Value`]
-sotonTenureDT[, MSOACode := `geography code`]
-
-sotonTenureDT[, hhCheck := census2011_socialRent + census2011_privateRent + census2011_ownerOccupy + census2011_other]
-sotonTenureDT[, nHHs_tenure := `Tenure: All households; measures: Value`]
-
-# summary(sotonTenureDT[, .(hhCheck, nHHs_tenure)])
-# might not quite match due to cell perturbation etc?
-
-# join em ----
-setkey(sotonDep_DT, MSOACode)
-setkey(sotonTenureDT, MSOACode)
-
-sotonCensus2011_DT <- sotonTenureDT[sotonDep_DT]
 
 t <- sotonCensus2011_DT[, .(sum_Deprivation = sum(nHHs_deprivation),
                             sum_Tenure = sum(nHHs_tenure)), keyby = .(LAName)]
@@ -393,13 +434,6 @@ That's lower (as expected) but doesn't allow for dwellings that were empty on ce
 ```{r, checkPostcodes}
 # Postcodes don't help - no count of addresses in the data (there used to be??)
 # but we can use it to check which Soton postcodes are missing from the EPC file
-soPostcodesDT <- data.table::fread(path.expand("~/data/UK_postcodes/NSPL_AUG_2020_UK/Data/multi_csv/NSPL_AUG_2020_UK_SO.csv"))
-
-soPostcodesDT <- soPostcodesDT[is.na(doterm)] # keep current
-
-sotonPostcodesDT <- soPostcodesDT[laua == "E06000045"] # keep Southampton City
-
-sotonPostcodesReducedDT <- sotonPostcodesDT[, .(pcd, pcd2, pcds, laua, msoa11, lsoa11)]
 
 sotonPostcodesReducedDT[, c("pc_chunk1","pc_chunk2" ) := tstrsplit(pcds, 
                                                                    split = " "
@@ -413,13 +447,13 @@ We should not have single digit postcodes in the postcode data - i.e. S01 should
 # EPC
 # set up counters
 # use final cleaned EPC data
-finalEPCDT[, epcIsSocialRent := ifelse(TENURE == "rental (social)", 1, 0)]
-finalEPCDT[, epcIsPrivateRent := ifelse(TENURE == "rental (private)", 1, 0)]
-finalEPCDT[, epcIsOwnerOcc := ifelse(TENURE == "owner-occupied", 1, 0)]
-finalEPCDT[, epcIsUnknownTenure := ifelse(TENURE == "NO DATA!" |
+sotonUniqueEPCsDT[, epcIsSocialRent := ifelse(TENURE == "rental (social)", 1, 0)]
+sotonUniqueEPCsDT[, epcIsPrivateRent := ifelse(TENURE == "rental (private)", 1, 0)]
+sotonUniqueEPCsDT[, epcIsOwnerOcc := ifelse(TENURE == "owner-occupied", 1, 0)]
+sotonUniqueEPCsDT[, epcIsUnknownTenure := ifelse(TENURE == "NO DATA!" |
                                           TENURE == "" , 1, 0)]
 # aggregate EPCs to postcodes
-sotonEpcPostcodes_DT <- finalEPCDT[, .(nEPCs = .N,
+sotonEpcPostcodes_DT <- sotonUniqueEPCsDT[, .(nEPCs = .N,
                                               sumEPC_tCO2 = sum(CO2_EMISSIONS_CURRENT, na.rm = TRUE),
                                      n_epcIsSocialRent = sum(epcIsSocialRent, na.rm = TRUE),
                                      n_epcIsPrivateRent = sum(epcIsPrivateRent, na.rm = TRUE),
@@ -435,11 +469,11 @@ sotonEpcPostcodes_DT[, c("pc_chunk1","pc_chunk2" ) := tstrsplit(POSTCODE,
 sotonEpcPostcodes_DT[, .(nEPCs = .N), keyby = .(pc_chunk1)]
 
 # check original EPC data for Soton - which postcodes are covered?
-allEPCs_DT[, c("pc_chunk1","pc_chunk2" ) := tstrsplit(POSTCODE, 
+sotonEPCsDT[, c("pc_chunk1","pc_chunk2" ) := tstrsplit(POSTCODE, 
                                                                    split = " "
                                                                    )
                         ]
-allEPCs_DT[, .(nEPCs = .N), keyby = .(pc_chunk1)]
+sotonEPCsDT[, .(nEPCs = .N), keyby = .(pc_chunk1)]
 ```
 It looks like we have EPCs for each postcode sector which is good.
 
@@ -479,7 +513,7 @@ Join the estimates together at MSOA level for comparison. There are `r uniqueN(s
 
 ```{r, joinMSOA}
 # 32 LSOAs in Soton
-# add deprivation
+# add census & deprivation to energy
 setkey(sotonEnergyDT, MSOACode)
 setkey(sotonCensus2011_DT, MSOACode)
 setkey(sotonEpcMSOA_DT, MSOACode)
@@ -494,8 +528,8 @@ sotonMSOA_DT <- sotonEpcMSOA_DT[sotonMSOA_DT]
 msoaNamesDT <- data.table::as.data.table(readxl::read_xlsx(path.expand("~/data/UK_postcodes/NSPL_AUG_2020_UK/Documents/MSOA (2011) names and codes UK as at 12_12.xlsx")))
 msoaNamesDT[, MSOACode := MSOA11CD]
 msoaNamesDT[, MSOAName := MSOA11NM]
-setkey(msoaNamesDT, MSOACode)
 
+setkey(msoaNamesDT, MSOACode)
 sotonMSOA_DT <- msoaNamesDT[sotonMSOA_DT]
 
 #names(sotonMSOA_DT)
@@ -530,11 +564,6 @@ We can also see that despite having 'missing' EPCs, the estimated total EPC-deri
 
 ```{r, missingEPCbyMSOA, fig.cap="% 'missing' rates comparison"}
 
-sotonMSOA_DT[, dep0_pc := 100*(`Household Deprivation: Household is not deprived in any dimension; measures: Value`/nHHs_deprivation)]
-sotonMSOA_DT[, socRent_pc := 100*(census2011_socialRent/nHHs_tenure)]
-sotonMSOA_DT[, privRent_pc := 100*(census2011_privateRent/nHHs_tenure)]
-sotonMSOA_DT[, ownerOcc_pc := 100*(census2011_ownerOccupy/nHHs_tenure)]
-
 t <- sotonMSOA_DT[, .(MSOAName, MSOACode, nHHs_tenure,nElecMeters,nEPCs,
                       dep0_pc, socRent_pc, privRent_pc, ownerOcc_pc,sumEpcMWh, beisEnergyMWh )]
 
@@ -585,7 +614,7 @@ outlier <- t[sumEpcMWh > 70000]
 
 Figure \@ref(fig:energyMSOAPlot) shows that both of these are true. MSOAs with a high proportion of owner occupiers (and therefore more likely to have missing EPCs) tend to have higher observed energy demand than the EOC data suggests - they are above the reference line. MSOAs with a lower proportion of owner occupiers (and therefore more likely to have more complete EPC coverage) tend to be on or below the line. As before we have the same notable outlier (`r outlier$MSOACode`) and for the same reasons... In this case this produces a much higher energy demand estimate than the BEIS 2018 data records.
 
-# Check BEIS data
+## BEIS: Check data
 
 While we're here we'll also check the BEIS data. Table \@ref(tab:beisDesc) shows the five highest and lowest MSOAs by annual electricity use.
 
@@ -604,7 +633,57 @@ kableExtra::kable(t2, caption = "Southampton MSOAs: BEIS 2018 energy data ordere
 ```
 
 
-# Save MSOA aggregates for re-use
+# Summarise and save EPC data for re-use
+
+We have identified some issues with a small number of the properties in the EPC dataset. These are not unexpected given that much of the estimates rely on partial or presumed data. Data entry errors are also quite likely. As a result we exclude:
+
+ * any property where ENERGY_CONSUMPTION_CURRENT <= 0
+ * any property where TOTAL_FLOOR_AREA <= 5
+ * any property where CO2_EMISSIONS_CURRENT <= 0
+
+```{r, finalData}
+finalEPCDT <- sotonUniqueEPCsDT[ENERGY_CONSUMPTION_CURRENT > 0 &
+                      TOTAL_FLOOR_AREA > 5 &
+                      CO2_EMISSIONS_CURRENT > 0]
+
+skimr::skim(finalEPCDT)
+```
+
+This leaves us with a total of `r prettyNum(nrow(finalEPCDT), big.mark = ",")` properties.
+
+```{r, saveFinalData}
+library(stringr)
+finalEPCDT[, POSTCODE_s := stringr::str_remove_all(POSTCODE, " ")]
+sotonPostcodesReducedDT[, POSTCODE_s := stringr::str_remove_all(pcds, " ")]
+setkey(finalEPCDT, POSTCODE_s)
+setkey(sotonPostcodesReducedDT, POSTCODE_s)
+dt <- sotonPostcodesReducedDT[finalEPCDT]
+dt[, MSOACode := msoa11]
+
+setkey(dt, MSOACode)
+setkey(sotonCensus2011_DT, MSOACode)
+
+dt <- sotonCensus2011_DT[dt]
+
+of <- path.expand("~/data/EW_epc/domestic-E06000045-Southampton/EPCs_liveFinalClean.csv")
+data.table::fwrite(dt, file = of)
+
+message("Gziping ", of)
+# Gzip it
+# in case it fails (it will on windows - you will be left with a .csv file)
+try(system( paste0("gzip -f '", of,"'"))) # include ' or it breaks on spaces
+message("Gzipped ", of)
+
+```
+
+NB: this failed to match an EPC postcode to an MSOA for `r nrow(dt[is.na(MSOACode)])` EPCs The table below shows which postcodes these were by date.
+
+```{r, nonMatches}
+dt[is.na(MSOACode), .(nEPCs = .N), keyby = .(POSTCODE_s, TENURE, INSPECTION_DATE)]
+```
+
+
+# Summarise and save MSOA aggregates for re-use
 
 Finally we save the MSOA table into the repo data directory for future use. We don't usually advocate keeping data in a git repo but this is small, aggregated and [mostly harmless](https://en.wikipedia.org/wiki/Mostly_Harmless).
 
diff --git a/docs/epcChecks.html b/docs/epcChecks.html
index 80f2799..7272c2a 100644
--- a/docs/epcChecks.html
+++ b/docs/epcChecks.html
@@ -1855,7 +1855,7 @@ div.tocify {
 <h1 class="title toc-ignore">Checking EPC datasets for Southampton</h1>
 <h3 class="subtitle">Data cleaning, outlier checks and coverage analysis</h3>
 <h4 class="author">Ben Anderson</h4>
-<h4 class="date">Last run at: 2020-11-10 17:31:09</h4>
+<h4 class="date">Last run at: 2020-11-11 17:02:14</h4>
 
 </div>
 
@@ -1876,13 +1876,11 @@ div.tocify {
 </div>
 <div id="data-loading" class="section level1">
 <h1><span class="header-section-number">2</span> Data loading</h1>
+<div id="epcs" class="section level2">
+<h2><span class="header-section-number">2.1</span> EPCs</h2>
 <p>Load the data for the area of interest - in this case the City of Southampton.</p>
 <pre class="r"><code>df &lt;- path.expand(&quot;~/data/EW_epc/domestic-E06000045-Southampton/certificates.csv&quot;)
-allEPCs_DT &lt;- data.table::fread(df)</code></pre>
-<pre><code>## Warning in require_bit64_if_needed(ans): Some columns are type &#39;integer64&#39; but package bit64 is not
-## installed. Those columns will print as strange looking floating point data. There is no need to reload
-## the data. Simply install.packages(&#39;bit64&#39;) to obtain the integer64 print method and print the data
-## again.</code></pre>
+sotonEPCsDT &lt;- data.table::fread(df)</code></pre>
 <p>The EPC data file has 91833 records for Southampton and 90 variables. We’re not interested in all of these, we want:</p>
 <ul>
 <li>PROPERTY_TYPE: Describes the type of property such as House, Flat, Maisonette etc. This is the type differentiator for dwellings;</li>
@@ -1902,10 +1900,10 @@ allEPCs_DT &lt;- data.table::fread(df)</code></pre>
 <li>INSPECTION_DATE - so we can select the most receitn</li>
 </ul>
 <p>These may indicate ‘non-grid’ energy inputs.</p>
-<div id="select-most-recent-records" class="section level2">
-<h2><span class="header-section-number">2.1</span> Select most recent records</h2>
+<div id="select-most-recent-records" class="section level3">
+<h3><span class="header-section-number">2.1.1</span> Select most recent records</h3>
 <p>If an EPC has been updated or refreshed, the EPC dataset will hold multiple EPC records for that property (see Table <a href="#tab:plotAllRecords">2.1</a>).</p>
-<pre class="r"><code>ggplot2::ggplot(allEPCs_DT, aes(x = INSPECTION_DATE)) +
+<pre class="r"><code>ggplot2::ggplot(sotonEPCsDT, aes(x = INSPECTION_DATE)) +
   geom_histogram()</code></pre>
 <pre><code>## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>
 <div class="figure"><span id="fig:plotAllRecords"></span>
@@ -1914,7 +1912,7 @@ allEPCs_DT &lt;- data.table::fread(df)</code></pre>
 Figure 2.1: All records: Inspection date
 </p>
 </div>
-<pre class="r"><code>t &lt;- allEPCs_DT[, .(nRecords = .N,
+<pre class="r"><code>t &lt;- sotonEPCsDT[, .(nRecords = .N,
                     firstDate = min(INSPECTION_DATE),
                     lastDate = max(INSPECTION_DATE)), keyby = .(BUILDING_REFERENCE_NUMBER)]
 
@@ -1942,7 +1940,7 @@ lastDate
 <tbody>
 <tr>
 <td style="text-align:right;">
-0
+444
 </td>
 <td style="text-align:right;">
 3
@@ -1956,7 +1954,7 @@ lastDate
 </tr>
 <tr>
 <td style="text-align:right;">
-0
+668
 </td>
 <td style="text-align:right;">
 2
@@ -1970,7 +1968,7 @@ lastDate
 </tr>
 <tr>
 <td style="text-align:right;">
-0
+697
 </td>
 <td style="text-align:right;">
 2
@@ -1984,7 +1982,7 @@ lastDate
 </tr>
 <tr>
 <td style="text-align:right;">
-0
+805
 </td>
 <td style="text-align:right;">
 2
@@ -1998,7 +1996,7 @@ lastDate
 </tr>
 <tr>
 <td style="text-align:right;">
-0
+871
 </td>
 <td style="text-align:right;">
 2
@@ -2012,7 +2010,7 @@ lastDate
 </tr>
 <tr>
 <td style="text-align:right;">
-0
+1362
 </td>
 <td style="text-align:right;">
 2
@@ -2028,7 +2026,7 @@ lastDate
 </table>
 <p>Figure <a href="#fig:plotAllRecords">2.1</a> shows the inspection date of all EPC records. We want to just select the most recent as we are not currently interested in change over time.</p>
 <pre class="r"><code># select just these vars
-dt &lt;- allEPCs_DT[, .(BUILDING_REFERENCE_NUMBER, LMK_KEY, LODGEMENT_DATE,INSPECTION_DATE, PROPERTY_TYPE, BUILT_FORM,
+dt &lt;- sotonEPCsDT[, .(BUILDING_REFERENCE_NUMBER, LMK_KEY, LODGEMENT_DATE,INSPECTION_DATE, PROPERTY_TYPE, BUILT_FORM,
                 ENVIRONMENT_IMPACT_CURRENT, ENERGY_CONSUMPTION_CURRENT, CO2_EMISSIONS_CURRENT, TENURE,
                 PHOTO_SUPPLY, WIND_TURBINE_COUNT, TOTAL_FLOOR_AREA, 
                 POSTCODE, LOCAL_AUTHORITY_LABEL)]
@@ -2063,8 +2061,8 @@ Figure 2.2: Latest records: Inspection date
 </p>
 </div>
 </div>
-<div id="descriptives" class="section level2">
-<h2><span class="header-section-number">2.2</span> Descriptives</h2>
+<div id="descriptives" class="section level3">
+<h3><span class="header-section-number">2.1.2</span> Descriptives</h3>
 <p>Now check the distributions of the retained variables.</p>
 <pre class="r"><code>skimr::skim(sotonUniqueEPCsDT)</code></pre>
 <pre><code>## Warning: Couldn&#39;t find skimmers for class: integer64; No user-defined `sfl` provided. Falling back to
@@ -2713,10 +2711,140 @@ TOTAL_FLOOR_AREA
 <p>This is not surprising since the kWh/y and TCO2/y values are estimated using a model but before we go any further we’d better check if these are significant in number.</p>
 </div>
 </div>
-<div id="epc-data-checks" class="section level1">
-<h1><span class="header-section-number">3</span> EPC data checks</h1>
-<div id="check-energy_consumption_current" class="section level2">
-<h2><span class="header-section-number">3.1</span> Check ENERGY_CONSUMPTION_CURRENT</h2>
+<div id="postcode-data" class="section level2">
+<h2><span class="header-section-number">2.2</span> Postcode data</h2>
+<p>Load postcodes for Southampton (contains other geo-codes for linkage).</p>
+<p>Source: <a href="https://geoportal.statistics.gov.uk/datasets/national-statistics-postcode-lookup-august-2020" class="uri">https://geoportal.statistics.gov.uk/datasets/national-statistics-postcode-lookup-august-2020</a></p>
+<pre class="r"><code># Load the postcode based MSOA codes
+soPostcodesDT &lt;- data.table::fread(path.expand(&quot;~/data/UK_postcodes/NSPL_AUG_2020_UK/Data/multi_csv/NSPL_AUG_2020_UK_SO.csv&quot;))
+
+#soPostcodesDT &lt;- soPostcodesDT[is.na(doterm)] # keep current
+# keep all as some of the defunct ones will be in the EPC data (!)
+
+sotonPostcodesDT &lt;- soPostcodesDT[laua == &quot;E06000045&quot;] # keep Southampton City
+
+sotonPostcodesReducedDT &lt;- sotonPostcodesDT[, .(pcd, pcd2, pcds, laua, msoa11, lsoa11)]
+
+message(&quot;Example data&quot;)</code></pre>
+<pre><code>## Example data</code></pre>
+<pre class="r"><code>head(sotonPostcodesReducedDT)</code></pre>
+<pre><code>##        pcd     pcd2    pcds      laua    msoa11    lsoa11
+## 1: SO1 0AA SO1  0AA SO1 0AA E06000045 E02003577 E01032748
+## 2: SO1 0AB SO1  0AB SO1 0AB E06000045 E02003577 E01032748
+## 3: SO1 0AD SO1  0AD SO1 0AD E06000045 E02003577 E01032748
+## 4: SO1 0AE SO1  0AE SO1 0AE E06000045 E02003571 E01017140
+## 5: SO1 0AF SO1  0AF SO1 0AF E06000045 E02003571 E01017140
+## 6: SO1 0AG SO1  0AG SO1 0AG E06000045 E02003577 E01032748</code></pre>
+</div>
+<div id="beis-data" class="section level2">
+<h2><span class="header-section-number">2.3</span> BEIS data</h2>
+<p>Load BEIS energy demand data.</p>
+<p>Source: <a href="https://geoportal.statistics.gov.uk/datasets/national-statistics-postcode-lookup-august-2020" class="uri">https://geoportal.statistics.gov.uk/datasets/national-statistics-postcode-lookup-august-2020</a></p>
+<pre class="r"><code>beisElecDT &lt;- data.table::fread(&quot;~/data/beis/MSOA_DOM_ELEC_csv/MSOA_ELEC_2018.csv&quot;)
+sotonElecDT &lt;- beisElecDT[LAName %like% &quot;Southampton&quot;, .(nElecMeters = METERS,  
+                                                         beisElecMWh = KWH/1000, 
+                                                         MSOACode, LAName)
+                          ]
+
+
+beisGasDT &lt;- data.table::fread(&quot;~/data/beis/MSOA_DOM_GAS_csv/MSOA_GAS_2018.csv&quot;)
+sotonGasDT &lt;- beisGasDT[LAName %like% &quot;Southampton&quot;, .(nGasMeters = METERS,  
+                                                         beisGasMWh = KWH/1000, 
+                                                         MSOACode)]
+
+setkey(sotonElecDT, MSOACode)
+setkey(sotonGasDT, MSOACode)
+sotonEnergyDT &lt;- sotonGasDT[sotonElecDT]
+sotonEnergyDT[, beisEnergyMWh := beisElecMWh + beisGasMWh]
+#head(sotonEnergyDT)
+message(&quot;Example data (retained variables)&quot;)</code></pre>
+<pre><code>## Example data (retained variables)</code></pre>
+<pre class="r"><code>head(sotonEnergyDT)</code></pre>
+<pre><code>##    nGasMeters beisGasMWh  MSOACode nElecMeters beisElecMWh      LAName beisEnergyMWh
+## 1:       2557   38480.93 E02003549        2832   11196.005 Southampton      49676.93
+## 2:       2876   28049.73 E02003550        3527   13074.440 Southampton      41124.17
+## 3:       1649   17358.87 E02003551        2446    8957.742 Southampton      26316.61
+## 4:       2009   17667.12 E02003552        2809   10383.889 Southampton      28051.01
+## 5:       2303   24996.91 E02003553        2464    8479.993 Southampton      33476.91
+## 6:       2378   29664.71 E02003554        2873   10048.060 Southampton      39712.77</code></pre>
+</div>
+<div id="census-data" class="section level2">
+<h2><span class="header-section-number">2.4</span> Census data</h2>
+<p>Load Census 2011 tenure data.</p>
+<p>Source: <a href="https://www.nomisweb.co.uk/census/2011/ks402ew" class="uri">https://www.nomisweb.co.uk/census/2011/ks402ew</a></p>
+<pre class="r"><code># census tenure ----
+dt &lt;- data.table::fread(path.expand(&quot;~/data/census2011/2011_MSOA_householdTenure_Soton.csv&quot;))
+
+dt[, census2011_socialRent := `Tenure: Social rented; measures: Value`]
+dt[, census2011_privateRent := `Tenure: Private rented; measures: Value`]
+dt[, census2011_ownerOccupy := `Tenure: Owned; measures: Value`]
+dt[, census2011_other := `Tenure: Living rent free; measures: Value`]
+dt[, MSOACode := `geography code`]
+
+dt[, hhCheck := census2011_socialRent + census2011_privateRent + census2011_ownerOccupy + census2011_other]
+dt[, nHHs_tenure := `Tenure: All households; measures: Value`]
+
+dt[, socRent_pc := 100*(census2011_socialRent/nHHs_tenure)]
+dt[, privRent_pc := 100*(census2011_privateRent/nHHs_tenure)]
+dt[, ownerOcc_pc := 100*(census2011_ownerOccupy/nHHs_tenure)]
+
+tenureDT &lt;- dt[, .(MSOACode, nHHs_tenure, socRent_pc, privRent_pc, ownerOcc_pc)]
+message(&quot;Example data (retained variables)&quot;)</code></pre>
+<pre><code>## Example data (retained variables)</code></pre>
+<pre class="r"><code>head(tenureDT) # all tenure data</code></pre>
+<pre><code>##     MSOACode nHHs_tenure socRent_pc privRent_pc ownerOcc_pc
+## 1: E02002559        3646   4.443225   16.346681    77.53703
+## 2: E02002560        2511   2.907208   12.385504    83.91079
+## 3: E02002561        2507  11.408057    8.575987    79.17830
+## 4: E02002562        2933  23.389021   26.321173    49.36925
+## 5: E02002563        2343  23.772941    8.664106    65.98378
+## 6: E02002564        4137  29.804206    8.581097    60.04351</code></pre>
+</div>
+<div id="deprivation-data" class="section level2">
+<h2><span class="header-section-number">2.5</span> Deprivation data</h2>
+<p>Load IMD data.</p>
+<p>Source: <a href="https://www.nomisweb.co.uk/census/2011/qs119ew" class="uri">https://www.nomisweb.co.uk/census/2011/qs119ew</a></p>
+<pre class="r"><code># add the  deprivation data by MSOA
+dt &lt;- data.table::fread(path.expand(&quot;~/data/census2011/2011_MSOA_deprivation.csv&quot;))
+dt[, nHHs_deprivation := `Household Deprivation: All categories: Classification of household deprivation; measures: Value`]
+dt[, MSOACode := `geography code`]
+
+#sotonDep_DT[, .(nHouseholds = sum(totalHouseholds)), keyby = .(LAName)]
+
+dt[, dep0_pc := 100*(`Household Deprivation: Household is not deprived in any dimension; measures: Value`/nHHs_deprivation)]
+dt[, dep1_pc := 100*(`Household Deprivation: Household is deprived in 1 dimension; measures: Value`/nHHs_deprivation)]
+dt[, dep2_pc := 100*(`Household Deprivation: Household is deprived in 2 dimensions; measures: Value`/nHHs_deprivation)]
+dt[, dep3_pc := 100*(`Household Deprivation: Household is deprived in 3 dimensions; measures: Value`/nHHs_deprivation)]
+dt[, dep4_pc := 100*(`Household Deprivation: Household is deprived in 4 dimensions; measures: Value`/nHHs_deprivation)]
+
+deprivationDT &lt;- dt[, .(MSOACode, nHHs_deprivation, dep0_pc, dep1_pc, dep2_pc, dep3_pc, dep4_pc)]
+# sneak the LA name in there too
+dt &lt;- sotonEnergyDT[,.(MSOACode,LAName)]
+setkey(dt, MSOACode)
+setkey(deprivationDT, MSOACode)
+
+sotonDeprivationDT &lt;- deprivationDT[dt] # has the side effect of dropping non-Soton MSOAs
+
+message(&quot;Example data (retained variables)&quot;)</code></pre>
+<pre><code>## Example data (retained variables)</code></pre>
+<pre class="r"><code>head(sotonDeprivationDT)</code></pre>
+<pre><code>##     MSOACode nHHs_deprivation  dep0_pc  dep1_pc  dep2_pc  dep3_pc   dep4_pc      LAName
+## 1: E02003549             2849 52.36925 32.88873 12.24991 2.316602 0.1755002 Southampton
+## 2: E02003550             3216 43.09701 32.92910 18.19030 5.254975 0.5286070 Southampton
+## 3: E02003551             2256 33.68794 33.99823 23.00532 8.289007 1.0195035 Southampton
+## 4: E02003552             2646 28.11791 32.01058 29.28949 9.901738 0.6802721 Southampton
+## 5: E02003553             2394 39.01420 32.95739 19.88304 6.975773 1.1695906 Southampton
+## 6: E02003554             2646 46.48526 32.38851 17.27135 3.514739 0.3401361 Southampton</code></pre>
+<pre class="r"><code># merge with census for future use
+setkey(sotonDeprivationDT, MSOACode)
+setkey(tenureDT, MSOACode)
+sotonCensus2011_DT &lt;- tenureDT[sotonDeprivationDT] # only Soton MSOAs</code></pre>
+</div>
+</div>
+<div id="data-checks" class="section level1">
+<h1><span class="header-section-number">3</span> Data checks</h1>
+<div id="epc-check-energy_consumption_current" class="section level2">
+<h2><span class="header-section-number">3.1</span> EPC: Check ENERGY_CONSUMPTION_CURRENT</h2>
 <p>We recode the current energy consumption into categories for comparison with other low values and the presence of wind turbines/PV. We use -ve, 0 and 1 kWh as the thresholds of interest.</p>
 <pre class="r"><code>ggplot2::ggplot(sotonUniqueEPCsDT, aes(x = ENERGY_CONSUMPTION_CURRENT)) +
   geom_histogram(binwidth = 5) + 
@@ -3039,8 +3167,8 @@ Figure 3.2: Comparing distributions of ENERGY_CONSUMPTION_CURRENT by tenure and
 </p>
 </div>
 </div>
-<div id="check-co2_emissions_current" class="section level2">
-<h2><span class="header-section-number">3.2</span> Check CO2_EMISSIONS_CURRENT</h2>
+<div id="epc-check-co2_emissions_current" class="section level2">
+<h2><span class="header-section-number">3.2</span> EPC: Check CO2_EMISSIONS_CURRENT</h2>
 <p>Next we do the same for current emissions. Repeat the coding for total floor area using 0 and 1 TCO2/y as the threshold of interest.</p>
 <pre class="r"><code>ggplot2::ggplot(sotonUniqueEPCsDT, aes(x = CO2_EMISSIONS_CURRENT)) +
   geom_histogram(binwidth = 1)</code></pre>
@@ -3441,10 +3569,10 @@ NA
 </table>
 <p>There are 22 properties with 0 or negative emissions. It looks like they are also the properties with -ve kWh as we might expect. So we can safely ignore them.</p>
 </div>
-<div id="check-environment_impact_current" class="section level2">
-<h2><span class="header-section-number">3.3</span> Check ENVIRONMENT_IMPACT_CURRENT</h2>
+<div id="epc-check-environment_impact_current" class="section level2">
+<h2><span class="header-section-number">3.3</span> EPC: Check ENVIRONMENT_IMPACT_CURRENT</h2>
 <p><code>Environmental impact</code> should decrease as emissions increase.</p>
-<pre class="r"><code>ggplot2::ggplot(allEPCs_DT, aes(x = ENVIRONMENT_IMPACT_CURRENT)) +
+<pre class="r"><code>ggplot2::ggplot(sotonEPCsDT, aes(x = ENVIRONMENT_IMPACT_CURRENT)) +
   geom_histogram()</code></pre>
 <pre><code>## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</code></pre>
 <div class="figure"><span id="fig:checkImpact"></span>
@@ -3454,7 +3582,7 @@ Figure 3.4: Histogram of ENVIRONMENT_IMPACT_CURRENT
 </p>
 </div>
 <p>So what is the relationship between ENVIRONMENT_IMPACT_CURRENT and CO2_EMISSIONS_CURRENT? It is not linear… (Figure <a href="#fig:checkEmissionsImpact">3.5</a>) and there are some interesting outliers.</p>
-<pre class="r"><code>ggplot2::ggplot(allEPCs_DT, aes(x = CO2_EMISSIONS_CURRENT, 
+<pre class="r"><code>ggplot2::ggplot(sotonEPCsDT, aes(x = CO2_EMISSIONS_CURRENT, 
                            y = ENVIRONMENT_IMPACT_CURRENT,
                            colour = TENURE)) +
   geom_point() +
@@ -3467,8 +3595,8 @@ Figure 3.5: Plot of ENVIRONMENT_IMPACT_CURRENT vs CO2_EMISSIONS_CURRENT
 </p>
 </div>
 </div>
-<div id="check-total_floor_area" class="section level2">
-<h2><span class="header-section-number">3.4</span> Check TOTAL_FLOOR_AREA</h2>
+<div id="epc-check-total_floor_area" class="section level2">
+<h2><span class="header-section-number">3.4</span> EPC: Check TOTAL_FLOOR_AREA</h2>
 <p>Repeat the coding for total floor area using 5 m2 as the threshold of interest.</p>
 <pre class="r"><code>ggplot2::ggplot(sotonUniqueEPCsDT, aes(x = TOTAL_FLOOR_AREA)) +
   geom_histogram(binwidth = 1)</code></pre>
@@ -3482,8 +3610,8 @@ Figure 3.6: Histogram of TOTAL_FLOOR_AREA
 
 sotonUniqueEPCsDT[, floorFlag := ifelse(TOTAL_FLOOR_AREA == 0, &quot;0 m2&quot;, NA)]
 sotonUniqueEPCsDT[, floorFlag := ifelse(TOTAL_FLOOR_AREA &gt; 0 &amp; 
-                                     TOTAL_FLOOR_AREA &lt;= 10, &quot;0-5 m2&quot;, floorFlag)]
-sotonUniqueEPCsDT[, floorFlag := ifelse(TOTAL_FLOOR_AREA &gt; 10, &quot;5+ m2&quot;, floorFlag)]
+                                     TOTAL_FLOOR_AREA &lt;= 5, &quot;0-5 m2&quot;, floorFlag)]
+sotonUniqueEPCsDT[, floorFlag := ifelse(TOTAL_FLOOR_AREA &gt; 5, &quot;5+ m2&quot;, floorFlag)]
 
 t &lt;- with(sotonUniqueEPCsDT, table(floorFlag, consFlag))
 
@@ -3533,7 +3661,7 @@ kableExtra::kable(round(100*prop.table(t),2), caption = &quot;% properties with
 0
 </td>
 <td style="text-align:right;">
-0.02
+0.00
 </td>
 </tr>
 <tr>
@@ -3547,7 +3675,7 @@ kableExtra::kable(round(100*prop.table(t),2), caption = &quot;% properties with
 0
 </td>
 <td style="text-align:right;">
-99.86
+99.87
 </td>
 </tr>
 </tbody>
@@ -3578,7 +3706,7 @@ ENERGY_CONSUMPTION_CURRENT
 <tbody>
 <tr>
 <td style="text-align:right;">
-4.697565e-314
+9507976768
 </td>
 <td style="text-align:left;">
 House
@@ -3592,7 +3720,7 @@ House
 </tr>
 <tr>
 <td style="text-align:right;">
-1.894551e-314
+3834614378
 </td>
 <td style="text-align:left;">
 House
@@ -3606,7 +3734,7 @@ House
 </tr>
 <tr>
 <td style="text-align:right;">
-4.846111e-314
+9808638568
 </td>
 <td style="text-align:left;">
 House
@@ -3620,7 +3748,7 @@ House
 </tr>
 <tr>
 <td style="text-align:right;">
-2.559778e-314
+5181048568
 </td>
 <td style="text-align:left;">
 House
@@ -3634,7 +3762,7 @@ House
 </tr>
 <tr>
 <td style="text-align:right;">
-8.172097e-315
+1654050778
 </td>
 <td style="text-align:left;">
 House
@@ -3648,7 +3776,7 @@ House
 </tr>
 <tr>
 <td style="text-align:right;">
-4.440838e-315
+898835568
 </td>
 <td style="text-align:left;">
 House
@@ -3662,7 +3790,7 @@ House
 </tr>
 <tr>
 <td style="text-align:right;">
-4.076249e-314
+8250419178
 </td>
 <td style="text-align:left;">
 House
@@ -3676,7 +3804,7 @@ House
 </tr>
 <tr>
 <td style="text-align:right;">
-1.933817e-314
+3914088378
 </td>
 <td style="text-align:left;">
 House
@@ -3690,7 +3818,7 @@ House
 </tr>
 <tr>
 <td style="text-align:right;">
-1.280057e-314
+2590863278
 </td>
 <td style="text-align:left;">
 House
@@ -3704,7 +3832,7 @@ House
 </tr>
 <tr>
 <td style="text-align:right;">
-2.444460e-314
+4947642078
 </td>
 <td style="text-align:left;">
 Flat
@@ -3744,7 +3872,7 @@ ENERGY_CONSUMPTION_CURRENT
 <tbody>
 <tr>
 <td style="text-align:right;">
-9.111592e-316
+184420668
 </td>
 <td style="text-align:left;">
 Flat
@@ -3758,7 +3886,7 @@ Flat
 </tr>
 <tr>
 <td style="text-align:right;">
-3.102124e-315
+627876968
 </td>
 <td style="text-align:left;">
 Flat
@@ -3772,7 +3900,7 @@ Flat
 </tr>
 <tr>
 <td style="text-align:right;">
-3.294384e-315
+666790668
 </td>
 <td style="text-align:left;">
 Flat
@@ -3786,7 +3914,7 @@ Flat
 </tr>
 <tr>
 <td style="text-align:right;">
-3.695003e-315
+747876968
 </td>
 <td style="text-align:left;">
 Flat
@@ -3800,7 +3928,7 @@ Flat
 </tr>
 <tr>
 <td style="text-align:right;">
-4.369619e-315
+884420668
 </td>
 <td style="text-align:left;">
 Flat
@@ -3814,7 +3942,7 @@ Flat
 </tr>
 <tr>
 <td style="text-align:right;">
-4.371685e-315
+884838868
 </td>
 <td style="text-align:left;">
 Flat
@@ -3828,7 +3956,7 @@ Flat
 </tr>
 <tr>
 <td style="text-align:right;">
-7.302298e-315
+1478001668
 </td>
 <td style="text-align:left;">
 Flat
@@ -3842,7 +3970,7 @@ Flat
 </tr>
 <tr>
 <td style="text-align:right;">
-9.515727e-315
+1926004568
 </td>
 <td style="text-align:left;">
 Flat
@@ -3856,7 +3984,7 @@ Flat
 </tr>
 <tr>
 <td style="text-align:right;">
-9.562249e-315
+1935420668
 </td>
 <td style="text-align:left;">
 Flat
@@ -3870,7 +3998,7 @@ Flat
 </tr>
 <tr>
 <td style="text-align:right;">
-1.059979e-314
+2145420668
 </td>
 <td style="text-align:left;">
 Flat
@@ -3930,7 +4058,7 @@ Flat
 0
 </td>
 <td style="text-align:right;">
-0.02
+0.00
 </td>
 </tr>
 <tr>
@@ -3944,7 +4072,7 @@ Flat
 0
 </td>
 <td style="text-align:right;">
-99.86
+99.87
 </td>
 </tr>
 </tbody>
@@ -3952,459 +4080,663 @@ Flat
 <p>Table <a href="#tab:checkEmissions">3.2</a> shows that the properties with floor area of &lt; 10m2 are not necessarily the ones with 0 or negative kWh values. Nevertheless they represent a small proportion of all properties.</p>
 <p>The scale of the x axis also suggests a few very large properties.</p>
 </div>
-<div id="data-summary" class="section level2">
-<h2><span class="header-section-number">3.5</span> Data summary</h2>
-<p>We have identified some issues with a small number of the properties in the EPC dataset. These are not unexpected given that much of the estimates rely on partial or presumed data. Data entry errors are also quite likely. As a result we exclude:</p>
-<ul>
-<li>any property where ENERGY_CONSUMPTION_CURRENT &lt;= 0</li>
-<li>any property where TOTAL_FLOOR_AREA &lt;= 5</li>
-<li>any property where CO2_EMISSIONS_CURRENT &lt;= 0</li>
-</ul>
-<pre class="r"><code>finalEPCDT &lt;- sotonUniqueEPCsDT[ENERGY_CONSUMPTION_CURRENT &gt; 0 &amp;
-                      TOTAL_FLOOR_AREA &gt; 5 &amp;
-                      CO2_EMISSIONS_CURRENT &gt; 0]
+<div id="epc-check-missing-epc-rates" class="section level2">
+<h2><span class="header-section-number">3.5</span> EPC: Check ‘missing’ EPC rates</h2>
+<p>We know that we do not have EPC records for every dwelling. But how many are we missing? We will check this at MSOA level as it allows us to link to other MSOA level datasets that tell us how many households, dwellings or energy meters to expect. Arguably it would be better to do this at LSOA level but…</p>
+<p>First we’ll use the BEIS 2018 MSOA level annual electricity data to estimate the number of meters (not properties) - some addresses can have 2 meters (e.g. standard &amp; economy 7). However this is more useful than the number of gas meters since not all dwellings have mains gas but all (should?) have an electricity meter.</p>
+<pre class="r"><code>sotonEnergyDT[, .(nElecMeters = sum(nElecMeters),
+                  nGasMeters = sum(nGasMeters)), keyby = .(LAName)]</code></pre>
+<pre><code>##         LAName nElecMeters nGasMeters
+## 1: Southampton      108333      81645</code></pre>
+<p>Next we’ll check for the number of households reported by the 2011 Census.</p>
+<blockquote>
+<p>would be better to use dwellings but this gives us tenure as well</p>
+</blockquote>
+<pre class="r"><code>#censusDT &lt;- data.table::fread(path.expand(&quot;~/data/&quot;))
 
-skimr::skim(finalEPCDT)</code></pre>
-<pre><code>## Warning: Couldn&#39;t find skimmers for class: integer64; No user-defined `sfl` provided. Falling back to
-## `character`.</code></pre>
-<table style="width: auto;" class="table table-condensed">
+t &lt;- sotonCensus2011_DT[, .(sum_Deprivation = sum(nHHs_deprivation),
+                            sum_Tenure = sum(nHHs_tenure)), keyby = .(LAName)]
+kableExtra::kable(t, caption = &quot;Census derived household counts&quot;)</code></pre>
+<table>
 <caption>
-<span id="tab:finalData">Table 3.4: </span>Data summary
+<span id="tab:checkCensus">Table 3.4: </span>Census derived household counts
 </caption>
 <thead>
 <tr>
 <th style="text-align:left;">
+LAName
 </th>
-<th style="text-align:left;">
+<th style="text-align:right;">
+sum_Deprivation
+</th>
+<th style="text-align:right;">
+sum_Tenure
 </th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <td style="text-align:left;">
-Name
-</td>
-<td style="text-align:left;">
-finalEPCDT
-</td>
-</tr>
-<tr>
-<td style="text-align:left;">
-Number of rows
-</td>
-<td style="text-align:left;">
-71502
-</td>
-</tr>
-<tr>
-<td style="text-align:left;">
-Number of columns
-</td>
-<td style="text-align:left;">
-20
-</td>
-</tr>
-<tr>
-<td style="text-align:left;">
-_______________________
-</td>
-<td style="text-align:left;">
-</td>
-</tr>
-<tr>
-<td style="text-align:left;">
-Column type frequency:
-</td>
-<td style="text-align:left;">
-</td>
-</tr>
-<tr>
-<td style="text-align:left;">
-character
-</td>
-<td style="text-align:left;">
-12
-</td>
-</tr>
-<tr>
-<td style="text-align:left;">
-Date
-</td>
-<td style="text-align:left;">
-2
-</td>
-</tr>
-<tr>
-<td style="text-align:left;">
-numeric
-</td>
-<td style="text-align:left;">
-6
-</td>
-</tr>
-<tr>
-<td style="text-align:left;">
-________________________
-</td>
-<td style="text-align:left;">
+Southampton
 </td>
-</tr>
-<tr>
-<td style="text-align:left;">
-Group variables
+<td style="text-align:right;">
+98254
 </td>
-<td style="text-align:left;">
-None
+<td style="text-align:right;">
+98254
 </td>
 </tr>
 </tbody>
 </table>
-<p><strong>Variable type: character</strong></p>
-<table>
+<p>That’s lower (as expected) but doesn’t allow for dwellings that were empty on census night.</p>
+<pre class="r"><code># Postcodes don&#39;t help - no count of addresses in the data (there used to be??)
+# but we can use it to check which Soton postcodes are missing from the EPC file
+
+sotonPostcodesReducedDT[, c(&quot;pc_chunk1&quot;,&quot;pc_chunk2&quot; ) := tstrsplit(pcds, 
+                                                                   split = &quot; &quot;
+                                                                   )
+                        ]
+sotonPostcodesReducedDT[, .(nEPCs = .N), keyby = .(pc_chunk1)]</code></pre>
+<pre><code>##     pc_chunk1 nEPCs
+##  1:       SO1  2343
+##  2:      SO14  1380
+##  3:      SO15  1801
+##  4:      SO16  1648
+##  5:      SO17   602
+##  6:      SO18  1208
+##  7:      SO19  1398
+##  8:       SO2  2737
+##  9:       SO3     3
+## 10:       SO4    13
+## 11:      SO45     2
+## 12:       SO9  1093</code></pre>
+<p>We should not have single digit postcodes in the postcode data - i.e. S01 should not be there (since 1993). Southampton City is unusual in only having <a href="https://en.wikipedia.org/wiki/SO_postcode_area">double digit postcodes</a>.</p>
+<pre class="r"><code># EPC
+# set up counters
+# use final cleaned EPC data
+sotonUniqueEPCsDT[, epcIsSocialRent := ifelse(TENURE == &quot;rental (social)&quot;, 1, 0)]
+sotonUniqueEPCsDT[, epcIsPrivateRent := ifelse(TENURE == &quot;rental (private)&quot;, 1, 0)]
+sotonUniqueEPCsDT[, epcIsOwnerOcc := ifelse(TENURE == &quot;owner-occupied&quot;, 1, 0)]
+sotonUniqueEPCsDT[, epcIsUnknownTenure := ifelse(TENURE == &quot;NO DATA!&quot; |
+                                          TENURE == &quot;&quot; , 1, 0)]
+# aggregate EPCs to postcodes
+sotonEpcPostcodes_DT &lt;- sotonUniqueEPCsDT[, .(nEPCs = .N,
+                                              sumEPC_tCO2 = sum(CO2_EMISSIONS_CURRENT, na.rm = TRUE),
+                                     n_epcIsSocialRent = sum(epcIsSocialRent, na.rm = TRUE),
+                                     n_epcIsPrivateRent = sum(epcIsPrivateRent, na.rm = TRUE),
+                                     n_epcIsOwnerOcc = sum(epcIsOwnerOcc, na.rm = TRUE),
+                                     n_epcIsUnknownTenure = sum(epcIsUnknownTenure, na.rm = TRUE),
+                               sumEpcMWh = sum(ENERGY_CONSUMPTION_CURRENT* TOTAL_FLOOR_AREA)/1000), # crucial as ENERGY_CONSUMPTION_CURRENT = kWh/m2
+                           keyby = .(POSTCODE, LOCAL_AUTHORITY_LABEL)]
+
+sotonEpcPostcodes_DT[, c(&quot;pc_chunk1&quot;,&quot;pc_chunk2&quot; ) := tstrsplit(POSTCODE, 
+                                                                   split = &quot; &quot;
+                                                                   )
+                        ]
+sotonEpcPostcodes_DT[, .(nEPCs = .N), keyby = .(pc_chunk1)]</code></pre>
+<pre><code>##    pc_chunk1 nEPCs
+## 1:      SO14   601
+## 2:      SO15   960
+## 3:      SO16  1245
+## 4:      SO17   403
+## 5:      SO18   776
+## 6:      SO19  1122</code></pre>
+<pre class="r"><code># check original EPC data for Soton - which postcodes are covered?
+sotonEPCsDT[, c(&quot;pc_chunk1&quot;,&quot;pc_chunk2&quot; ) := tstrsplit(POSTCODE, 
+                                                                   split = &quot; &quot;
+                                                                   )
+                        ]
+sotonEPCsDT[, .(nEPCs = .N), keyby = .(pc_chunk1)]</code></pre>
+<pre><code>##    pc_chunk1 nEPCs
+## 1:      SO14 14213
+## 2:      SO15 17855
+## 3:      SO16 20270
+## 4:      SO17  8446
+## 5:      SO18 10661
+## 6:      SO19 20388</code></pre>
+<p>It looks like we have EPCs for each postcode sector which is good.</p>
+<pre class="r"><code># match the EPC postcode summaries to the postcode extract
+sotonPostcodesReducedDT[, POSTCODE_s := stringr::str_remove(pcds, &quot; &quot;)]
+setkey(sotonPostcodesReducedDT, POSTCODE_s)
+sotonPostcodesReducedDT[, MSOACode := msoa11]
+message(&quot;Number of postcodes: &quot;,uniqueN(sotonPostcodesReducedDT$POSTCODE_s))</code></pre>
+<pre><code>## Number of postcodes: 14228</code></pre>
+<pre class="r"><code>sotonEpcPostcodes_DT[, POSTCODE_s := stringr::str_remove(POSTCODE, &quot; &quot;)]
+setkey(sotonEpcPostcodes_DT, POSTCODE_s)
+message(&quot;Number of postcodes with EPCs: &quot;,uniqueN(sotonEpcPostcodes_DT$POSTCODE_s))</code></pre>
+<pre><code>## Number of postcodes with EPCs: 5107</code></pre>
+<pre class="r"><code>dt &lt;- sotonEpcPostcodes_DT[sotonPostcodesReducedDT]
+
+# aggregate to MSOA - watch for NAs where no EPCs in a given postcode
+sotonEpcMSOA_DT &lt;- dt[, .(nEPCs = sum(nEPCs, na.rm = TRUE), 
+                          sumEPC_tCO2 = sum(sumEPC_tCO2, na.rm = TRUE),
+                                        n_epcIsSocialRent = sum(n_epcIsSocialRent, na.rm = TRUE),
+                                        n_epcIsPrivateRent = sum(n_epcIsPrivateRent, na.rm = TRUE),
+                                        n_epcIsOwnerOcc = sum(n_epcIsOwnerOcc, na.rm = TRUE),
+                                        n_epcIsUnknownTenure = sum(n_epcIsUnknownTenure, na.rm = TRUE),
+                                        sumEpcMWh = sum(sumEpcMWh, na.rm = TRUE)
+                                        ),
+                                    keyby = .(MSOACode) # change name on the fly for easier matching
+                                    ] 
+
+#summary(sotonEpcMSOA_DT)</code></pre>
+<p>So we have some postcodes with no EPCs.</p>
+<p>Join the estimates together at MSOA level for comparison. There are 32 MSOAs in Southampton.</p>
+<pre class="r"><code># 32 LSOAs in Soton
+# add census &amp; deprivation to energy
+setkey(sotonEnergyDT, MSOACode)
+setkey(sotonCensus2011_DT, MSOACode)
+setkey(sotonEpcMSOA_DT, MSOACode)
+
+sotonMSOA_DT &lt;- sotonCensus2011_DT[sotonEnergyDT]
+#names(sotonMSOA_DT)
+sotonMSOA_DT &lt;- sotonEpcMSOA_DT[sotonMSOA_DT]
+#names(sotonMSOA_DT)
+
+# add MSOA names from the postcode LUT
+
+msoaNamesDT &lt;- data.table::as.data.table(readxl::read_xlsx(path.expand(&quot;~/data/UK_postcodes/NSPL_AUG_2020_UK/Documents/MSOA (2011) names and codes UK as at 12_12.xlsx&quot;)))
+msoaNamesDT[, MSOACode := MSOA11CD]
+msoaNamesDT[, MSOAName := MSOA11NM]
+
+setkey(msoaNamesDT, MSOACode)
+sotonMSOA_DT &lt;- msoaNamesDT[sotonMSOA_DT]
+
+#names(sotonMSOA_DT)</code></pre>
+<pre class="r"><code>t &lt;- sotonMSOA_DT[, .(nHouseholds_2011 = sum(nHHs_tenure),
+                      nElecMeters_2018 = sum(nElecMeters),
+                      nEPCs_2020 = sum(nEPCs)), keyby = .(LAName)]
+
+kableExtra::kable(t, caption = &quot;Comparison of different estimates of the number of dwellings&quot;) %&gt;%
+  kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+<span id="tab:compareEpcEstimates">Table 3.5: </span>Comparison of different estimates of the number of dwellings
+</caption>
 <thead>
 <tr>
 <th style="text-align:left;">
-skim_variable
+LAName
 </th>
 <th style="text-align:right;">
-n_missing
+nHouseholds_2011
 </th>
 <th style="text-align:right;">
-complete_rate
+nElecMeters_2018
 </th>
 <th style="text-align:right;">
-min
+nEPCs_2020
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+Southampton
+</td>
+<td style="text-align:right;">
+98254
+</td>
+<td style="text-align:right;">
+108333
+</td>
+<td style="text-align:right;">
+71527
+</td>
+</tr>
+</tbody>
+</table>
+<pre class="r"><code>nHouseholds_2011f &lt;- sum(sotonMSOA_DT$nHHs_tenure)
+nElecMeters_2018f &lt;- sum(sotonMSOA_DT$nElecMeters)
+nEPCs_2020f &lt;- sum(sotonMSOA_DT$nEPCs)
+
+makePC &lt;- function(x,y,r){
+  # make a percent of x/y and round it to r decimal places
+  pc &lt;- round(100*(x/y),r)
+  return(pc)
+}</code></pre>
+<p>From this we calculate that number of EPCs we have is:</p>
+<ul>
+<li>72.8% of Census 2011 households
+<ul>
+<li>66% of the recorded 2018 electricity meters</li>
+</ul></li>
+</ul>
+<p>We can also see that despite having ‘missing’ EPCs, the estimated total EPC-derived energy demand is marginally higher than the BEIS-derived weather corrected energy demand data. Given that the BEIS data accounts for all heating, cooking, hot water, lighting and appliance use we would expect the EPC data to be lower <em>even if no EPCs were missing…</em></p>
+<pre class="r"><code>t &lt;- sotonMSOA_DT[, .(MSOAName, MSOACode, nHHs_tenure,nElecMeters,nEPCs,
+                      dep0_pc, socRent_pc, privRent_pc, ownerOcc_pc,sumEpcMWh, beisEnergyMWh )]
+
+t[, pc_missingHH := makePC(nEPCs,nHHs_tenure,1)]
+t[, pc_missingMeters := makePC(nEPCs,nElecMeters,1)]
+t[, pc_energyBEIS := makePC(sumEpcMWh,beisEnergyMWh,1)]
+
+kt1 &lt;- t
+
+ggplot2::ggplot(t, aes(x = pc_missingHH, 
+                       y = pc_missingMeters,
+                       colour = round(ownerOcc_pc))) +
+  geom_abline(alpha = 0.2, slope=1, intercept=0) +
+  geom_point() +
+  scale_color_continuous(name = &quot;% owner occupiers \n(Census 2011)&quot;, high = &quot;red&quot;, low = &quot;green&quot;) +
+  #theme(legend.position = &quot;bottom&quot;) +
+  labs(x = &quot;EPCs 2020 as % of Census 2011 households&quot;,
+       y = &quot;EPCs 2020 as % of electricity meters 2018&quot;,
+       caption = &quot;x = y line included for clarity&quot;)</code></pre>
+<div class="figure"><span id="fig:missingEPCbyMSOA"></span>
+<img src="data:image/png;base64,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" alt="% &#39;missing&#39; rates comparison" width="672" />
+<p class="caption">
+Figure 3.7: % ‘missing’ rates comparison
+</p>
+</div>
+<pre class="r"><code>outlierMSOA &lt;- t[pc_missingHH &gt; 100]</code></pre>
+<p>Figure <a href="#fig:missingEPCbyMSOA">3.7</a> (see Table <a href="#tab:bigMSOATable">7.1</a> below for details) suggests that rates vary considerably by MSOA but are relatively consistent across the two baseline ‘truth’ estimates with the exception of E02003577 which appears to have many more EPCs than Census 2011 households. It is worth noting that <a href="https://www.localhealth.org.uk/#c=report&amp;chapter=c01&amp;report=r01&amp;selgeo1=msoa_2011.E02003577&amp;selgeo2=eng.E92000001">this MSOA</a> covers the city centre and dock areas which have had substantial new build since 2011 and so may have households inhabiting dwellings that did not exist at Census 2011. This is also supported by the considerably higher EPC derived energy demand data compared to BEIS’s 2018 data - although it suggests the dwellings are either very new (since 2018) or are yet to be occupied.</p>
+<p>As we would expect those MSOAs with the lowest EPC coverage on both baseline measures tend to have higher proportions of owner occupiers.</p>
+<p>We can use the same approach to compare estimates of total energy demand at the MSOA level. To do this we compare:</p>
+<ul>
+<li>estimated total energy demand in MWh/year derived from the EPC estimates. This energy only relates to <code>current primary energy</code> (space heating, hot water and lighting) and of course also suffers from missing EPCs (see above)</li>
+<li>observed electricity and gas demand collated by BEIS for their sub-national statistical series. This applies to all domestic energy demand but the most recent data is for 2018 so will suffer from the absence of dwellings that are present in the most recent EPC data (see above).</li>
+</ul>
+<p>We should therefore not expect the values to match but we might reasonably expect a correlation.</p>
+<pre class="r"><code>ggplot2::ggplot(t, aes(x = sumEpcMWh, 
+                       y = beisEnergyMWh,
+                       colour = round(ownerOcc_pc))) +
+  geom_abline(alpha = 0.2, slope=1, intercept=0) +
+  geom_point() +
+  scale_color_continuous(name = &quot;% owner occupiers \n(Census 2011)&quot;, high = &quot;red&quot;, low = &quot;green&quot;) +
+  #theme(legend.position = &quot;bottom&quot;) +
+  labs(x = &quot;EPC 2020 derived total MWh/year&quot;,
+       y = &quot;BEIS 2018 derived total MWh/year&quot;,
+       caption = &quot;x = y line included for clarity&quot;)</code></pre>
+<div class="figure"><span id="fig:energyMSOAPlot"></span>
+<img src="data:image/png;base64,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" alt="Energy demand comparison" width="672" />
+<p class="caption">
+Figure 3.8: Energy demand comparison
+</p>
+</div>
+<pre class="r"><code>outlier &lt;- t[sumEpcMWh &gt; 70000]</code></pre>
+<p>Figure <a href="#fig:energyMSOAPlot">3.8</a> shows that both of these are true. MSOAs with a high proportion of owner occupiers (and therefore more likely to have missing EPCs) tend to have higher observed energy demand than the EOC data suggests - they are above the reference line. MSOAs with a lower proportion of owner occupiers (and therefore more likely to have more complete EPC coverage) tend to be on or below the line. As before we have the same notable outlier (E02003577) and for the same reasons… In this case this produces a much higher energy demand estimate than the BEIS 2018 data records.</p>
+</div>
+<div id="beis-check-data" class="section level2">
+<h2><span class="header-section-number">3.6</span> BEIS: Check data</h2>
+<p>While we’re here we’ll also check the BEIS data. Table <a href="#tab:beisDesc">3.6</a> shows the five highest and lowest MSOAs by annual electricity use.</p>
+<pre class="r"><code>t1 &lt;- head(sotonMSOA_DT[, .(MSOA11NM, MSOA11CD, beisElecMWh, nElecMeters,
+                            beisGasMWh, nGasMeters)][order(-beisElecMWh)],5)
+
+kableExtra::kable(t1, caption = &quot;Southampton MSOAs: BEIS 2018 energy data ordered by highest electricity (top 5)&quot;) %&gt;%
+  kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+<span id="tab:beisDesc">Table 3.6: </span>Southampton MSOAs: BEIS 2018 energy data ordered by highest electricity (top 5)
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+MSOA11NM
+</th>
+<th style="text-align:left;">
+MSOA11CD
 </th>
 <th style="text-align:right;">
-max
+beisElecMWh
 </th>
 <th style="text-align:right;">
-empty
+nElecMeters
 </th>
 <th style="text-align:right;">
-n_unique
+beisGasMWh
 </th>
 <th style="text-align:right;">
-whitespace
+nGasMeters
 </th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <td style="text-align:left;">
-BUILDING_REFERENCE_NUMBER
-</td>
-<td style="text-align:right;">
-0
-</td>
-<td style="text-align:right;">
-1.00
+Southampton 029
 </td>
-<td style="text-align:right;">
-17
+<td style="text-align:left;">
+E02003577
 </td>
 <td style="text-align:right;">
-21
+27352.70
 </td>
 <td style="text-align:right;">
-0
+6734
 </td>
 <td style="text-align:right;">
-71502
+20108.63
 </td>
 <td style="text-align:right;">
-0
+2420
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-LMK_KEY
-</td>
-<td style="text-align:right;">
-0
-</td>
-<td style="text-align:right;">
-1.00
+Southampton 014
 </td>
-<td style="text-align:right;">
-29
+<td style="text-align:left;">
+E02003562
 </td>
 <td style="text-align:right;">
-34
+14757.18
 </td>
 <td style="text-align:right;">
-0
+3921
 </td>
 <td style="text-align:right;">
-71502
+36532.48
 </td>
 <td style="text-align:right;">
-0
+2983
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-PROPERTY_TYPE
-</td>
-<td style="text-align:right;">
-0
-</td>
-<td style="text-align:right;">
-1.00
+Southampton 022
 </td>
-<td style="text-align:right;">
-4
+<td style="text-align:left;">
+E02003570
 </td>
 <td style="text-align:right;">
-10
+14719.37
 </td>
 <td style="text-align:right;">
-0
+4142
 </td>
 <td style="text-align:right;">
-5
+34730.60
 </td>
 <td style="text-align:right;">
-0
+3083
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-BUILT_FORM
-</td>
-<td style="text-align:right;">
-0
-</td>
-<td style="text-align:right;">
-1.00
+Southampton 031
 </td>
-<td style="text-align:right;">
-8
+<td style="text-align:left;">
+E02003579
 </td>
 <td style="text-align:right;">
-20
+13860.94
 </td>
 <td style="text-align:right;">
-0
+4460
 </td>
 <td style="text-align:right;">
-7
+34052.12
 </td>
 <td style="text-align:right;">
-0
+3068
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-TENURE
-</td>
-<td style="text-align:right;">
-0
-</td>
-<td style="text-align:right;">
-1.00
+Southampton 021
 </td>
-<td style="text-align:right;">
-0
+<td style="text-align:left;">
+E02003569
 </td>
 <td style="text-align:right;">
-16
+13719.22
 </td>
 <td style="text-align:right;">
-1905
+3999
 </td>
 <td style="text-align:right;">
-6
+27661.45
 </td>
 <td style="text-align:right;">
-0
+2722
 </td>
 </tr>
+</tbody>
+</table>
+<pre class="r"><code>t2 &lt;- tail(sotonMSOA_DT[, .(MSOA11NM, MSOA11CD, beisElecMWh, nElecMeters,
+                            beisGasMWh, nGasMeters)][order(-beisElecMWh)],5)
+
+kableExtra::kable(t2, caption = &quot;Southampton MSOAs: BEIS 2018 energy data ordered by lowest electricity (bottom 5)&quot;) %&gt;%
+  kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+<span id="tab:beisDesc">Table 3.6: </span>Southampton MSOAs: BEIS 2018 energy data ordered by lowest electricity (bottom 5)
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+MSOA11NM
+</th>
+<th style="text-align:left;">
+MSOA11CD
+</th>
+<th style="text-align:right;">
+beisElecMWh
+</th>
+<th style="text-align:right;">
+nElecMeters
+</th>
+<th style="text-align:right;">
+beisGasMWh
+</th>
+<th style="text-align:right;">
+nGasMeters
+</th>
+</tr>
+</thead>
+<tbody>
 <tr>
 <td style="text-align:left;">
-POSTCODE
-</td>
-<td style="text-align:right;">
-0
+Southampton 024
 </td>
-<td style="text-align:right;">
-1.00
+<td style="text-align:left;">
+E02003572
 </td>
 <td style="text-align:right;">
-8
+9347.893
 </td>
 <td style="text-align:right;">
-8
+2597
 </td>
 <td style="text-align:right;">
-0
+30332.49
 </td>
 <td style="text-align:right;">
-5105
-</td>
-<td style="text-align:right;">
-0
+2381
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-LOCAL_AUTHORITY_LABEL
-</td>
-<td style="text-align:right;">
-0
-</td>
-<td style="text-align:right;">
-1.00
+Southampton 018
 </td>
-<td style="text-align:right;">
-11
+<td style="text-align:left;">
+E02003566
 </td>
 <td style="text-align:right;">
-11
+9221.544
 </td>
 <td style="text-align:right;">
-0
+2831
 </td>
 <td style="text-align:right;">
-1
+26826.22
 </td>
 <td style="text-align:right;">
-0
+2607
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-hasWind
-</td>
-<td style="text-align:right;">
-5546
-</td>
-<td style="text-align:right;">
-0.92
+Southampton 008
 </td>
-<td style="text-align:right;">
-2
+<td style="text-align:left;">
+E02003556
 </td>
 <td style="text-align:right;">
-3
+9199.673
 </td>
 <td style="text-align:right;">
-0
+2589
 </td>
 <td style="text-align:right;">
-2
+26412.36
 </td>
 <td style="text-align:right;">
-0
+2295
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-hasPV
-</td>
-<td style="text-align:right;">
-38495
-</td>
-<td style="text-align:right;">
-0.46
+Southampton 003
 </td>
-<td style="text-align:right;">
-2
+<td style="text-align:left;">
+E02003551
 </td>
 <td style="text-align:right;">
-3
+8957.742
 </td>
 <td style="text-align:right;">
-0
+2446
 </td>
 <td style="text-align:right;">
-2
+17358.87
 </td>
 <td style="text-align:right;">
-0
+1649
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-consFlag
+Southampton 005
 </td>
-<td style="text-align:right;">
-0
+<td style="text-align:left;">
+E02003553
 </td>
 <td style="text-align:right;">
-1.00
+8479.993
 </td>
 <td style="text-align:right;">
-8
+2464
 </td>
 <td style="text-align:right;">
-8
+24996.91
 </td>
 <td style="text-align:right;">
-0
+2303
 </td>
-<td style="text-align:right;">
-1
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+<div id="summarise-and-save-epc-data-for-re-use" class="section level1">
+<h1><span class="header-section-number">4</span> Summarise and save EPC data for re-use</h1>
+<p>We have identified some issues with a small number of the properties in the EPC dataset. These are not unexpected given that much of the estimates rely on partial or presumed data. Data entry errors are also quite likely. As a result we exclude:</p>
+<ul>
+<li>any property where ENERGY_CONSUMPTION_CURRENT &lt;= 0</li>
+<li>any property where TOTAL_FLOOR_AREA &lt;= 5</li>
+<li>any property where CO2_EMISSIONS_CURRENT &lt;= 0</li>
+</ul>
+<pre class="r"><code>finalEPCDT &lt;- sotonUniqueEPCsDT[ENERGY_CONSUMPTION_CURRENT &gt; 0 &amp;
+                      TOTAL_FLOOR_AREA &gt; 5 &amp;
+                      CO2_EMISSIONS_CURRENT &gt; 0]
+
+skimr::skim(finalEPCDT)</code></pre>
+<pre><code>## Warning: Couldn&#39;t find skimmers for class: integer64; No user-defined `sfl` provided. Falling back to
+## `character`.</code></pre>
+<table style="width: auto;" class="table table-condensed">
+<caption>
+<span id="tab:finalData">Table 4.1: </span>Data summary
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+</th>
+<th style="text-align:left;">
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+Name
 </td>
-<td style="text-align:right;">
-0
+<td style="text-align:left;">
+finalEPCDT
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-emissionsFlag
+Number of rows
 </td>
-<td style="text-align:right;">
-0
+<td style="text-align:left;">
+71502
 </td>
-<td style="text-align:right;">
-1.00
+</tr>
+<tr>
+<td style="text-align:left;">
+Number of columns
 </td>
-<td style="text-align:right;">
-9
+<td style="text-align:left;">
+24
 </td>
-<td style="text-align:right;">
-10
+</tr>
+<tr>
+<td style="text-align:left;">
+_______________________
 </td>
-<td style="text-align:right;">
-0
+<td style="text-align:left;">
 </td>
-<td style="text-align:right;">
-2
+</tr>
+<tr>
+<td style="text-align:left;">
+Column type frequency:
 </td>
-<td style="text-align:right;">
-0
+<td style="text-align:left;">
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-floorFlag
+character
 </td>
-<td style="text-align:right;">
-0
+<td style="text-align:left;">
+12
 </td>
-<td style="text-align:right;">
-1.00
+</tr>
+<tr>
+<td style="text-align:left;">
+Date
 </td>
-<td style="text-align:right;">
-5
+<td style="text-align:left;">
+2
 </td>
-<td style="text-align:right;">
-6
+</tr>
+<tr>
+<td style="text-align:left;">
+numeric
 </td>
-<td style="text-align:right;">
-0
+<td style="text-align:left;">
+10
 </td>
-<td style="text-align:right;">
-2
+</tr>
+<tr>
+<td style="text-align:left;">
+________________________
 </td>
-<td style="text-align:right;">
-0
+<td style="text-align:left;">
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+Group variables
+</td>
+<td style="text-align:left;">
+None
 </td>
 </tr>
 </tbody>
 </table>
-<p><strong>Variable type: Date</strong></p>
+<p><strong>Variable type: character</strong></p>
 <table>
 <thead>
 <tr>
@@ -4417,112 +4749,79 @@ n_missing
 <th style="text-align:right;">
 complete_rate
 </th>
-<th style="text-align:left;">
+<th style="text-align:right;">
 min
 </th>
-<th style="text-align:left;">
+<th style="text-align:right;">
 max
 </th>
-<th style="text-align:left;">
-median
+<th style="text-align:right;">
+empty
 </th>
 <th style="text-align:right;">
 n_unique
 </th>
+<th style="text-align:right;">
+whitespace
+</th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <td style="text-align:left;">
-LODGEMENT_DATE
+BUILDING_REFERENCE_NUMBER
 </td>
 <td style="text-align:right;">
 0
 </td>
 <td style="text-align:right;">
-1
+1.00
 </td>
-<td style="text-align:left;">
-2008-10-01
+<td style="text-align:right;">
+17
 </td>
-<td style="text-align:left;">
-2020-06-30
+<td style="text-align:right;">
+21
 </td>
-<td style="text-align:left;">
-2014-10-22
+<td style="text-align:right;">
+0
 </td>
 <td style="text-align:right;">
-4132
+71502
+</td>
+<td style="text-align:right;">
+0
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-INSPECTION_DATE
+LMK_KEY
 </td>
 <td style="text-align:right;">
 0
 </td>
 <td style="text-align:right;">
-1
+1.00
 </td>
-<td style="text-align:left;">
-2007-03-02
+<td style="text-align:right;">
+29
 </td>
-<td style="text-align:left;">
-2020-06-30
+<td style="text-align:right;">
+34
 </td>
-<td style="text-align:left;">
-2014-10-14
+<td style="text-align:right;">
+0
 </td>
 <td style="text-align:right;">
-3906
+71502
+</td>
+<td style="text-align:right;">
+0
 </td>
 </tr>
-</tbody>
-</table>
-<p><strong>Variable type: numeric</strong></p>
-<table>
-<thead>
-<tr>
-<th style="text-align:left;">
-skim_variable
-</th>
-<th style="text-align:right;">
-n_missing
-</th>
-<th style="text-align:right;">
-complete_rate
-</th>
-<th style="text-align:right;">
-mean
-</th>
-<th style="text-align:right;">
-sd
-</th>
-<th style="text-align:right;">
-p0
-</th>
-<th style="text-align:right;">
-p25
-</th>
-<th style="text-align:right;">
-p50
-</th>
-<th style="text-align:right;">
-p75
-</th>
-<th style="text-align:right;">
-p100
-</th>
-<th style="text-align:left;">
-hist
-</th>
-</tr>
-</thead>
-<tbody>
 <tr>
 <td style="text-align:left;">
-ENVIRONMENT_IMPACT_CURRENT
+PROPERTY_TYPE
 </td>
 <td style="text-align:right;">
 0
@@ -4531,33 +4830,50 @@ ENVIRONMENT_IMPACT_CURRENT
 1.00
 </td>
 <td style="text-align:right;">
-62.51
+4
 </td>
 <td style="text-align:right;">
-15.72
+10
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+5
+</td>
+<td style="text-align:right;">
+0
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+BUILT_FORM
+</td>
+<td style="text-align:right;">
+0
 </td>
 <td style="text-align:right;">
 1.00
 </td>
 <td style="text-align:right;">
-52.0
+8
 </td>
 <td style="text-align:right;">
-63.00
+20
 </td>
 <td style="text-align:right;">
-73
+0
 </td>
 <td style="text-align:right;">
-100.00
+7
 </td>
-<td style="text-align:left;">
-▁▂▆▇▂
+<td style="text-align:right;">
+0
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-ENERGY_CONSUMPTION_CURRENT
+TENURE
 </td>
 <td style="text-align:right;">
 0
@@ -4566,33 +4882,50 @@ ENERGY_CONSUMPTION_CURRENT
 1.00
 </td>
 <td style="text-align:right;">
-263.23
+0
 </td>
 <td style="text-align:right;">
-140.47
+16
 </td>
 <td style="text-align:right;">
-4.00
+1905
 </td>
 <td style="text-align:right;">
-174.0
+6
 </td>
 <td style="text-align:right;">
-233.00
+0
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+POSTCODE
 </td>
 <td style="text-align:right;">
-327
+0
 </td>
 <td style="text-align:right;">
-1597.00
+1.00
 </td>
-<td style="text-align:left;">
-▇▂▁▁▁
+<td style="text-align:right;">
+8
+</td>
+<td style="text-align:right;">
+8
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+5105
+</td>
+<td style="text-align:right;">
+0
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-CO2_EMISSIONS_CURRENT
+LOCAL_AUTHORITY_LABEL
 </td>
 <td style="text-align:right;">
 0
@@ -4601,33 +4934,50 @@ CO2_EMISSIONS_CURRENT
 1.00
 </td>
 <td style="text-align:right;">
-3.17
+11
 </td>
 <td style="text-align:right;">
-1.94
+11
 </td>
 <td style="text-align:right;">
-0.10
+0
 </td>
 <td style="text-align:right;">
-1.8
+1
 </td>
 <td style="text-align:right;">
-2.85
+0
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+hasWind
 </td>
 <td style="text-align:right;">
-4
+5546
 </td>
 <td style="text-align:right;">
-77.00
+0.92
 </td>
-<td style="text-align:left;">
-▇▁▁▁▁
+<td style="text-align:right;">
+2
+</td>
+<td style="text-align:right;">
+3
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+2
+</td>
+<td style="text-align:right;">
+0
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-PHOTO_SUPPLY
+hasPV
 </td>
 <td style="text-align:right;">
 38495
@@ -4636,68 +4986,76 @@ PHOTO_SUPPLY
 0.46
 </td>
 <td style="text-align:right;">
-0.59
+2
 </td>
 <td style="text-align:right;">
-5.11
+3
 </td>
 <td style="text-align:right;">
-0.00
+0
 </td>
 <td style="text-align:right;">
-0.0
+2
 </td>
 <td style="text-align:right;">
-0.00
+0
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+consFlag
 </td>
 <td style="text-align:right;">
 0
 </td>
 <td style="text-align:right;">
-100.00
+1.00
 </td>
-<td style="text-align:left;">
-▇▁▁▁▁
+<td style="text-align:right;">
+8
 </td>
-</tr>
-<tr>
-<td style="text-align:left;">
-WIND_TURBINE_COUNT
+<td style="text-align:right;">
+8
 </td>
 <td style="text-align:right;">
-5546
+0
 </td>
 <td style="text-align:right;">
-0.92
+1
 </td>
 <td style="text-align:right;">
-0.00
+0
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+emissionsFlag
 </td>
 <td style="text-align:right;">
-0.02
+0
 </td>
 <td style="text-align:right;">
--1.00
+1.00
 </td>
 <td style="text-align:right;">
-0.0
+9
 </td>
 <td style="text-align:right;">
-0.00
+10
 </td>
 <td style="text-align:right;">
 0
 </td>
 <td style="text-align:right;">
-1.00
+2
 </td>
-<td style="text-align:left;">
-▁▁▇▁▁
+<td style="text-align:right;">
+0
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-TOTAL_FLOOR_AREA
+floorFlag
 </td>
 <td style="text-align:right;">
 0
@@ -4706,646 +5064,543 @@ TOTAL_FLOOR_AREA
 1.00
 </td>
 <td style="text-align:right;">
-73.05
+5
 </td>
 <td style="text-align:right;">
-34.86
+5
 </td>
 <td style="text-align:right;">
-5.85
+0
 </td>
 <td style="text-align:right;">
-49.0
+1
 </td>
 <td style="text-align:right;">
-69.00
+0
+</td>
+</tr>
+</tbody>
+</table>
+<p><strong>Variable type: Date</strong></p>
+<table>
+<thead>
+<tr>
+<th style="text-align:left;">
+skim_variable
+</th>
+<th style="text-align:right;">
+n_missing
+</th>
+<th style="text-align:right;">
+complete_rate
+</th>
+<th style="text-align:left;">
+min
+</th>
+<th style="text-align:left;">
+max
+</th>
+<th style="text-align:left;">
+median
+</th>
+<th style="text-align:right;">
+n_unique
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+LODGEMENT_DATE
 </td>
 <td style="text-align:right;">
-87
+0
 </td>
 <td style="text-align:right;">
-1353.68
+1
 </td>
 <td style="text-align:left;">
-▇▁▁▁▁
+2008-10-01
 </td>
-</tr>
-</tbody>
-</table>
-<p>This leaves us with a total of 71,502 properties.</p>
-<pre class="r"><code>of &lt;- path.expand(&quot;~/data/EW_epc/domestic-E06000045-Southampton/finalClean.csv&quot;)
-data.table::fwrite(finalEPCDT, file = of)
-
-message(&quot;Gziping &quot;, of)</code></pre>
-<pre><code>## Gziping /Users/ben/data/EW_epc/domestic-E06000045-Southampton/finalClean.csv</code></pre>
-<pre class="r"><code># Gzip it
-# in case it fails (it will on windows - you will be left with a .csv file)
-try(system( paste0(&quot;gzip -f &#39;&quot;, of,&quot;&#39;&quot;))) # include &#39; or it breaks on spaces
-message(&quot;Gzipped &quot;, of)</code></pre>
-<pre><code>## Gzipped /Users/ben/data/EW_epc/domestic-E06000045-Southampton/finalClean.csv</code></pre>
-</div>
-</div>
-<div id="check-missing-epc-rates" class="section level1">
-<h1><span class="header-section-number">4</span> Check ‘missing’ EPC rates</h1>
-<p>We know that we do not have EPC records for every dwelling. But how many are we missing? We will check this at MSOA level as it allows us to link to other MSOA level datasets that tell us how many households, dwellings or energy meters to expect. Arguably it would be better to do this at LSOA level but…</p>
-<p>First we’ll use the BEIS 2018 MSOA level annual electricity data to estimate the number of meters (not properties) - some addresses can have 2 meters (e.g. standard &amp; economy 7). This is more useful than the number of gas meters since not all dwellings have mains gas but all have an electricity meter.</p>
-<pre class="r"><code>beisElecDT &lt;- data.table::fread(&quot;~/data/beis/MSOA_DOM_ELEC_csv/MSOA_ELEC_2018.csv&quot;)
-sotonElecDT &lt;- beisElecDT[LAName %like% &quot;Southampton&quot;, .(nElecMeters = METERS,  
-                                                         beisElecMWh = KWH/1000, 
-                                                         MSOACode, LAName)
-                          ]
-
-
-beisGasDT &lt;- data.table::fread(&quot;~/data/beis/MSOA_DOM_GAS_csv/MSOA_GAS_2018.csv&quot;)
-sotonGasDT &lt;- beisGasDT[LAName %like% &quot;Southampton&quot;, .(nGasMeters = METERS,  
-                                                         beisGasMWh = KWH/1000, 
-                                                         MSOACode)]
-
-setkey(sotonElecDT, MSOACode)
-setkey(sotonGasDT, MSOACode)
-sotonEnergyDT &lt;- sotonGasDT[sotonElecDT]
-sotonEnergyDT[, beisEnergyMWh := beisElecMWh + beisGasMWh]
-#head(sotonEnergyDT)</code></pre>
-<p>Next we’ll check for the number of households reported by the 2011 Census.</p>
-<blockquote>
-<p>would be better to use dwellings but this gives us tenure</p>
-</blockquote>
-<pre class="r"><code>#censusDT &lt;- data.table::fread(path.expand(&quot;~/data/&quot;))
-# IMD ----
-deprivationDT &lt;- data.table::fread(path.expand(&quot;~/data/census2011/2011_MSOA_deprivation.csv&quot;))
-deprivationDT[, totalHouseholds := `Household Deprivation: All categories: Classification of household deprivation; measures: Value`]
-deprivationDT[, MSOACode := `geography code`]
-setkey(deprivationDT, MSOACode)
-setkey(sotonElecDT, MSOACode)
-# link LA name from Soton elec for now
-sotonDep_DT &lt;- deprivationDT[sotonElecDT[, .(MSOACode, LAName)]]
-sotonDep_DT[, nHHs_deprivation := `Household Deprivation: All categories: Classification of household deprivation; measures: Value`]
-
-#sotonDep_DT[, .(nHouseholds = sum(totalHouseholds)), keyby = .(LAName)]
-
-# census tenure ----
-sotonTenureDT &lt;- data.table::fread(path.expand(&quot;~/data/census2011/2011_MSOA_householdTenure_Soton.csv&quot;))
-
-sotonTenureDT[, census2011_socialRent := `Tenure: Social rented; measures: Value`]
-sotonTenureDT[, census2011_privateRent := `Tenure: Private rented; measures: Value`]
-sotonTenureDT[, census2011_ownerOccupy := `Tenure: Owned; measures: Value`]
-sotonTenureDT[, census2011_other := `Tenure: Living rent free; measures: Value`]
-sotonTenureDT[, MSOACode := `geography code`]
-
-sotonTenureDT[, hhCheck := census2011_socialRent + census2011_privateRent + census2011_ownerOccupy + census2011_other]
-sotonTenureDT[, nHHs_tenure := `Tenure: All households; measures: Value`]
-
-# summary(sotonTenureDT[, .(hhCheck, nHHs_tenure)])
-# might not quite match due to cell perturbation etc?
-
-# join em ----
-setkey(sotonDep_DT, MSOACode)
-setkey(sotonTenureDT, MSOACode)
-
-sotonCensus2011_DT &lt;- sotonTenureDT[sotonDep_DT]
-
-t &lt;- sotonCensus2011_DT[, .(sum_Deprivation = sum(nHHs_deprivation),
-                            sum_Tenure = sum(nHHs_tenure)), keyby = .(LAName)]
-kableExtra::kable(t, caption = &quot;Census derived household counts&quot;)</code></pre>
-<table>
-<caption>
-<span id="tab:checkCensus">Table 4.1: </span>Census derived household counts
-</caption>
-<thead>
-<tr>
-<th style="text-align:left;">
-LAName
-</th>
-<th style="text-align:right;">
-sum_Deprivation
-</th>
-<th style="text-align:right;">
-sum_Tenure
-</th>
-</tr>
-</thead>
-<tbody>
-<tr>
 <td style="text-align:left;">
-Southampton
+2020-06-30
 </td>
-<td style="text-align:right;">
-98254
+<td style="text-align:left;">
+2014-10-22
 </td>
 <td style="text-align:right;">
-98254
+4132
 </td>
 </tr>
-</tbody>
-</table>
-<p>That’s lower (as expected) but doesn’t allow for dwellings that were empty on census night.</p>
-<pre class="r"><code># Postcodes don&#39;t help - no count of addresses in the data (there used to be??)
-# but we can use it to check which Soton postcodes are missing from the EPC file
-soPostcodesDT &lt;- data.table::fread(path.expand(&quot;~/data/UK_postcodes/NSPL_AUG_2020_UK/Data/multi_csv/NSPL_AUG_2020_UK_SO.csv&quot;))
-
-soPostcodesDT &lt;- soPostcodesDT[is.na(doterm)] # keep current
-
-sotonPostcodesDT &lt;- soPostcodesDT[laua == &quot;E06000045&quot;] # keep Southampton City
-
-sotonPostcodesReducedDT &lt;- sotonPostcodesDT[, .(pcd, pcd2, pcds, laua, msoa11, lsoa11)]
-
-sotonPostcodesReducedDT[, c(&quot;pc_chunk1&quot;,&quot;pc_chunk2&quot; ) := tstrsplit(pcds, 
-                                                                   split = &quot; &quot;
-                                                                   )
-                        ]
-sotonPostcodesReducedDT[, .(nEPCs = .N), keyby = .(pc_chunk1)]</code></pre>
-<pre><code>##    pc_chunk1 nEPCs
-## 1:      SO14   849
-## 2:      SO15  1176
-## 3:      SO16  1328
-## 4:      SO17   443
-## 5:      SO18   859
-## 6:      SO19  1164</code></pre>
-<p>We should not have single digit postcodes in the postcode data - i.e. S01 should not be there (since 1993). Southampton City is unusual in only having <a href="https://en.wikipedia.org/wiki/SO_postcode_area">double digit postcodes</a>.</p>
-<pre class="r"><code># EPC
-# set up counters
-# use final cleaned EPC data
-finalEPCDT[, epcIsSocialRent := ifelse(TENURE == &quot;rental (social)&quot;, 1, 0)]
-finalEPCDT[, epcIsPrivateRent := ifelse(TENURE == &quot;rental (private)&quot;, 1, 0)]
-finalEPCDT[, epcIsOwnerOcc := ifelse(TENURE == &quot;owner-occupied&quot;, 1, 0)]
-finalEPCDT[, epcIsUnknownTenure := ifelse(TENURE == &quot;NO DATA!&quot; |
-                                          TENURE == &quot;&quot; , 1, 0)]
-# aggregate EPCs to postcodes
-sotonEpcPostcodes_DT &lt;- finalEPCDT[, .(nEPCs = .N,
-                                              sumEPC_tCO2 = sum(CO2_EMISSIONS_CURRENT, na.rm = TRUE),
-                                     n_epcIsSocialRent = sum(epcIsSocialRent, na.rm = TRUE),
-                                     n_epcIsPrivateRent = sum(epcIsPrivateRent, na.rm = TRUE),
-                                     n_epcIsOwnerOcc = sum(epcIsOwnerOcc, na.rm = TRUE),
-                                     n_epcIsUnknownTenure = sum(epcIsUnknownTenure, na.rm = TRUE),
-                               sumEpcMWh = sum(ENERGY_CONSUMPTION_CURRENT* TOTAL_FLOOR_AREA)/1000), # crucial as ENERGY_CONSUMPTION_CURRENT = kWh/m2
-                           keyby = .(POSTCODE, LOCAL_AUTHORITY_LABEL)]
-
-sotonEpcPostcodes_DT[, c(&quot;pc_chunk1&quot;,&quot;pc_chunk2&quot; ) := tstrsplit(POSTCODE, 
-                                                                   split = &quot; &quot;
-                                                                   )
-                        ]
-sotonEpcPostcodes_DT[, .(nEPCs = .N), keyby = .(pc_chunk1)]</code></pre>
-<pre><code>##    pc_chunk1 nEPCs
-## 1:      SO14   601
-## 2:      SO15   960
-## 3:      SO16  1244
-## 4:      SO17   403
-## 5:      SO18   775
-## 6:      SO19  1122</code></pre>
-<pre class="r"><code># check original EPC data for Soton - which postcodes are covered?
-allEPCs_DT[, c(&quot;pc_chunk1&quot;,&quot;pc_chunk2&quot; ) := tstrsplit(POSTCODE, 
-                                                                   split = &quot; &quot;
-                                                                   )
-                        ]
-allEPCs_DT[, .(nEPCs = .N), keyby = .(pc_chunk1)]</code></pre>
-<pre><code>##    pc_chunk1 nEPCs
-## 1:      SO14 14213
-## 2:      SO15 17855
-## 3:      SO16 20270
-## 4:      SO17  8446
-## 5:      SO18 10661
-## 6:      SO19 20388</code></pre>
-<p>It looks like we have EPCs for each postcode sector which is good.</p>
-<pre class="r"><code># match the EPC postcode summaries to the postcode extract
-sotonPostcodesReducedDT[, POSTCODE_s := stringr::str_remove(pcds, &quot; &quot;)]
-setkey(sotonPostcodesReducedDT, POSTCODE_s)
-sotonPostcodesReducedDT[, MSOACode := msoa11]
-message(&quot;Number of postcodes: &quot;,uniqueN(sotonPostcodesReducedDT$POSTCODE_s))</code></pre>
-<pre><code>## Number of postcodes: 5819</code></pre>
-<pre class="r"><code>sotonEpcPostcodes_DT[, POSTCODE_s := stringr::str_remove(POSTCODE, &quot; &quot;)]
-setkey(sotonEpcPostcodes_DT, POSTCODE_s)
-message(&quot;Number of postcodes with EPCs: &quot;,uniqueN(sotonEpcPostcodes_DT$POSTCODE_s))</code></pre>
-<pre><code>## Number of postcodes with EPCs: 5105</code></pre>
-<pre class="r"><code>dt &lt;- sotonEpcPostcodes_DT[sotonPostcodesReducedDT]
-
-# aggregate to MSOA - watch for NAs where no EPCs in a given postcode
-sotonEpcMSOA_DT &lt;- dt[, .(nEPCs = sum(nEPCs, na.rm = TRUE), 
-                          sumEPC_tCO2 = sum(sumEPC_tCO2, na.rm = TRUE),
-                                        n_epcIsSocialRent = sum(n_epcIsSocialRent, na.rm = TRUE),
-                                        n_epcIsPrivateRent = sum(n_epcIsPrivateRent, na.rm = TRUE),
-                                        n_epcIsOwnerOcc = sum(n_epcIsOwnerOcc, na.rm = TRUE),
-                                        n_epcIsUnknownTenure = sum(n_epcIsUnknownTenure, na.rm = TRUE),
-                                        sumEpcMWh = sum(sumEpcMWh, na.rm = TRUE)
-                                        ),
-                                    keyby = .(MSOACode) # change name on the fly for easier matching
-                                    ] 
-
-#summary(sotonEpcMSOA_DT)</code></pre>
-<p>So we have some postcodes with no EPCs.</p>
-<p>Join the estimates together at MSOA level for comparison. There are 32 MSOAs in Southampton.</p>
-<pre class="r"><code># 32 LSOAs in Soton
-# add deprivation
-setkey(sotonEnergyDT, MSOACode)
-setkey(sotonCensus2011_DT, MSOACode)
-setkey(sotonEpcMSOA_DT, MSOACode)
-
-sotonMSOA_DT &lt;- sotonCensus2011_DT[sotonEnergyDT]
-#names(sotonMSOA_DT)
-sotonMSOA_DT &lt;- sotonEpcMSOA_DT[sotonMSOA_DT]
-#names(sotonMSOA_DT)
-
-# add MSOA names from the postcode LUT
-
-msoaNamesDT &lt;- data.table::as.data.table(readxl::read_xlsx(path.expand(&quot;~/data/UK_postcodes/NSPL_AUG_2020_UK/Documents/MSOA (2011) names and codes UK as at 12_12.xlsx&quot;)))
-msoaNamesDT[, MSOACode := MSOA11CD]
-msoaNamesDT[, MSOAName := MSOA11NM]
-setkey(msoaNamesDT, MSOACode)
-
-sotonMSOA_DT &lt;- msoaNamesDT[sotonMSOA_DT]
-
-#names(sotonMSOA_DT)</code></pre>
-<pre class="r"><code>t &lt;- sotonMSOA_DT[, .(nHouseholds_2011 = sum(nHHs_tenure),
-                      nElecMeters_2018 = sum(nElecMeters),
-                      nEPCs_2020 = sum(nEPCs)), keyby = .(LAName)]
-
-kableExtra::kable(t, caption = &quot;Comparison of different estimates of the number of dwellings&quot;) %&gt;%
-  kable_styling()</code></pre>
-<table class="table" style="margin-left: auto; margin-right: auto;">
-<caption>
-<span id="tab:compareEpcEstimates">Table 4.2: </span>Comparison of different estimates of the number of dwellings
-</caption>
-<thead>
-<tr>
-<th style="text-align:left;">
-LAName
-</th>
-<th style="text-align:right;">
-nHouseholds_2011
-</th>
-<th style="text-align:right;">
-nElecMeters_2018
-</th>
-<th style="text-align:right;">
-nEPCs_2020
-</th>
-</tr>
-</thead>
-<tbody>
 <tr>
 <td style="text-align:left;">
-Southampton
+INSPECTION_DATE
 </td>
 <td style="text-align:right;">
-98254
+0
 </td>
 <td style="text-align:right;">
-108333
+1
+</td>
+<td style="text-align:left;">
+2007-03-02
+</td>
+<td style="text-align:left;">
+2020-06-30
+</td>
+<td style="text-align:left;">
+2014-10-14
 </td>
 <td style="text-align:right;">
-71179
+3906
 </td>
 </tr>
 </tbody>
 </table>
-<pre class="r"><code>nHouseholds_2011f &lt;- sum(sotonMSOA_DT$nHHs_tenure)
-nElecMeters_2018f &lt;- sum(sotonMSOA_DT$nElecMeters)
-nEPCs_2020f &lt;- sum(sotonMSOA_DT$nEPCs)
-
-makePC &lt;- function(x,y,r){
-  # make a percent of x/y and round it to r decimal places
-  pc &lt;- round(100*(x/y),r)
-  return(pc)
-}</code></pre>
-<p>From this we calculate that number of EPCs we have is:</p>
-<ul>
-<li>72.4% of Census 2011 households
-<ul>
-<li>65.7% of the recorded 2018 electricity meters</li>
-</ul></li>
-</ul>
-<p>We can also see that despite having ‘missing’ EPCs, the estimated total EPC-derived energy demand is marginally higher than the BEIS-derived weather corrected energy demand data. Given that the BEIS data accounts for all heating, cooking, hot water, lighting and appliance use we would expect the EPC data to be lower <em>even if no EPCs were missing…</em></p>
-<pre class="r"><code>sotonMSOA_DT[, dep0_pc := 100*(`Household Deprivation: Household is not deprived in any dimension; measures: Value`/nHHs_deprivation)]
-sotonMSOA_DT[, socRent_pc := 100*(census2011_socialRent/nHHs_tenure)]
-sotonMSOA_DT[, privRent_pc := 100*(census2011_privateRent/nHHs_tenure)]
-sotonMSOA_DT[, ownerOcc_pc := 100*(census2011_ownerOccupy/nHHs_tenure)]
-
-t &lt;- sotonMSOA_DT[, .(MSOAName, MSOACode, nHHs_tenure,nElecMeters,nEPCs,
-                      dep0_pc, socRent_pc, privRent_pc, ownerOcc_pc,sumEpcMWh, beisEnergyMWh )]
-
-t[, pc_missingHH := makePC(nEPCs,nHHs_tenure,1)]
-t[, pc_missingMeters := makePC(nEPCs,nElecMeters,1)]
-t[, pc_energyBEIS := makePC(sumEpcMWh,beisEnergyMWh,1)]
-
-kt1 &lt;- t
-
-ggplot2::ggplot(t, aes(x = pc_missingHH, 
-                       y = pc_missingMeters,
-                       colour = round(ownerOcc_pc))) +
-  geom_abline(alpha = 0.2, slope=1, intercept=0) +
-  geom_point() +
-  scale_color_continuous(name = &quot;% owner occupiers \n(Census 2011)&quot;, high = &quot;red&quot;, low = &quot;green&quot;) +
-  #theme(legend.position = &quot;bottom&quot;) +
-  labs(x = &quot;EPCs 2020 as % of Census 2011 households&quot;,
-       y = &quot;EPCs 2020 as % of electricity meters 2018&quot;,
-       caption = &quot;x = y line included for clarity&quot;)</code></pre>
-<div class="figure"><span id="fig:missingEPCbyMSOA"></span>
-<img src="data:image/png;base64,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" alt="% &#39;missing&#39; rates comparison" width="672" />
-<p class="caption">
-Figure 4.1: % ‘missing’ rates comparison
-</p>
-</div>
-<pre class="r"><code>outlierMSOA &lt;- t[pc_missingHH &gt; 100]</code></pre>
-<p>Figure <a href="#fig:missingEPCbyMSOA">4.1</a> (see Table <a href="#tab:bigMSOATable">8.1</a> below for details) suggests that rates vary considerably by MSOA but are relatively consistent across the two baseline ‘truth’ estimates with the exception of E02003577 which appears to have many more EPCs than Census 2011 households. It is worth noting that <a href="https://www.localhealth.org.uk/#c=report&amp;chapter=c01&amp;report=r01&amp;selgeo1=msoa_2011.E02003577&amp;selgeo2=eng.E92000001">this MSOA</a> covers the city centre and dock areas which have had substantial new build since 2011 and so may have households inhabiting dwellings that did not exist at Census 2011. This is also supported by the considerably higher EPC derived energy demand data compared to BEIS’s 2018 data - although it suggests the dwellings are either very new (since 2018) or are yet to be occupied.</p>
-<p>As we would expect those MSOAs with the lowest EPC coverage on both baseline measures tend to have higher proportions of owner occupiers.</p>
-<p>We can use the same approach to compare estimates of total energy demand at the MSOA level. To do this we compare:</p>
-<ul>
-<li>estimated total energy demand in MWh/year derived from the EPC estimates. This energy only relates to <code>current primary energy</code> (space heating, hot water and lighting) and of course also suffers from missing EPCs (see above)</li>
-<li>observed electricity and gas demand collated by BEIS for their sub-national statistical series. This applies to all domestic energy demand but the most recent data is for 2018 so will suffer from the absence of dwellings that are present in the most recent EPC data (see above).</li>
-</ul>
-<p>We should therefore not expect the values to match but we might reasonably expect a correlation.</p>
-<pre class="r"><code>ggplot2::ggplot(t, aes(x = sumEpcMWh, 
-                       y = beisEnergyMWh,
-                       colour = round(ownerOcc_pc))) +
-  geom_abline(alpha = 0.2, slope=1, intercept=0) +
-  geom_point() +
-  scale_color_continuous(name = &quot;% owner occupiers \n(Census 2011)&quot;, high = &quot;red&quot;, low = &quot;green&quot;) +
-  #theme(legend.position = &quot;bottom&quot;) +
-  labs(x = &quot;EPC 2020 derived total MWh/year&quot;,
-       y = &quot;BEIS 2018 derived total MWh/year&quot;,
-       caption = &quot;x = y line included for clarity&quot;)</code></pre>
-<div class="figure"><span id="fig:energyMSOAPlot"></span>
-<img src="data:image/png;base64,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" alt="Energy demand comparison" width="672" />
-<p class="caption">
-Figure 4.2: Energy demand comparison
-</p>
-</div>
-<pre class="r"><code>outlier &lt;- t[sumEpcMWh &gt; 70000]</code></pre>
-<p>Figure <a href="#fig:energyMSOAPlot">4.2</a> shows that both of these are true. MSOAs with a high proportion of owner occupiers (and therefore more likely to have missing EPCs) tend to have higher observed energy demand than the EOC data suggests - they are above the reference line. MSOAs with a lower proportion of owner occupiers (and therefore more likely to have more complete EPC coverage) tend to be on or below the line. As before we have the same notable outlier (E02003577) and for the same reasons… In this case this produces a much higher energy demand estimate than the BEIS 2018 data records.</p>
-</div>
-<div id="check-beis-data" class="section level1">
-<h1><span class="header-section-number">5</span> Check BEIS data</h1>
-<p>While we’re here we’ll also check the BEIS data. Table <a href="#tab:beisDesc">5.1</a> shows the five highest and lowest MSOAs by annual electricity use.</p>
-<pre class="r"><code>t1 &lt;- head(sotonMSOA_DT[, .(MSOA11NM, MSOA11CD, beisElecMWh, nElecMeters,
-                            beisGasMWh, nGasMeters)][order(-beisElecMWh)],5)
-
-kableExtra::kable(t1, caption = &quot;Southampton MSOAs: BEIS 2018 energy data ordered by highest electricity (top 5)&quot;) %&gt;%
-  kable_styling()</code></pre>
-<table class="table" style="margin-left: auto; margin-right: auto;">
-<caption>
-<span id="tab:beisDesc">Table 5.1: </span>Southampton MSOAs: BEIS 2018 energy data ordered by highest electricity (top 5)
-</caption>
+<p><strong>Variable type: numeric</strong></p>
+<table>
 <thead>
 <tr>
 <th style="text-align:left;">
-MSOA11NM
+skim_variable
 </th>
-<th style="text-align:left;">
-MSOA11CD
+<th style="text-align:right;">
+n_missing
 </th>
 <th style="text-align:right;">
-beisElecMWh
+complete_rate
 </th>
 <th style="text-align:right;">
-nElecMeters
+mean
 </th>
 <th style="text-align:right;">
-beisGasMWh
+sd
 </th>
 <th style="text-align:right;">
-nGasMeters
+p0
+</th>
+<th style="text-align:right;">
+p25
+</th>
+<th style="text-align:right;">
+p50
+</th>
+<th style="text-align:right;">
+p75
+</th>
+<th style="text-align:right;">
+p100
+</th>
+<th style="text-align:left;">
+hist
 </th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <td style="text-align:left;">
-Southampton 029
+ENVIRONMENT_IMPACT_CURRENT
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+1.00
+</td>
+<td style="text-align:right;">
+62.51
+</td>
+<td style="text-align:right;">
+15.72
+</td>
+<td style="text-align:right;">
+1.00
+</td>
+<td style="text-align:right;">
+52.0
+</td>
+<td style="text-align:right;">
+63.00
+</td>
+<td style="text-align:right;">
+73
+</td>
+<td style="text-align:right;">
+100.00
+</td>
+<td style="text-align:left;">
+▁▂▆▇▂
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+ENERGY_CONSUMPTION_CURRENT
 </td>
-<td style="text-align:left;">
-E02003577
+<td style="text-align:right;">
+0
 </td>
 <td style="text-align:right;">
-27352.70
+1.00
 </td>
 <td style="text-align:right;">
-6734
+263.23
 </td>
 <td style="text-align:right;">
-20108.63
+140.47
 </td>
 <td style="text-align:right;">
-2420
+4.00
+</td>
+<td style="text-align:right;">
+174.0
+</td>
+<td style="text-align:right;">
+233.00
+</td>
+<td style="text-align:right;">
+327
+</td>
+<td style="text-align:right;">
+1597.00
+</td>
+<td style="text-align:left;">
+▇▂▁▁▁
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-Southampton 014
+CO2_EMISSIONS_CURRENT
 </td>
-<td style="text-align:left;">
-E02003562
+<td style="text-align:right;">
+0
 </td>
 <td style="text-align:right;">
-14757.18
+1.00
 </td>
 <td style="text-align:right;">
-3921
+3.17
 </td>
 <td style="text-align:right;">
-36532.48
+1.94
 </td>
 <td style="text-align:right;">
-2983
+0.10
+</td>
+<td style="text-align:right;">
+1.8
+</td>
+<td style="text-align:right;">
+2.85
+</td>
+<td style="text-align:right;">
+4
+</td>
+<td style="text-align:right;">
+77.00
+</td>
+<td style="text-align:left;">
+▇▁▁▁▁
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-Southampton 022
+PHOTO_SUPPLY
 </td>
-<td style="text-align:left;">
-E02003570
+<td style="text-align:right;">
+38495
 </td>
 <td style="text-align:right;">
-14719.37
+0.46
 </td>
 <td style="text-align:right;">
-4142
+0.59
 </td>
 <td style="text-align:right;">
-34730.60
+5.11
 </td>
 <td style="text-align:right;">
-3083
+0.00
+</td>
+<td style="text-align:right;">
+0.0
+</td>
+<td style="text-align:right;">
+0.00
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+100.00
+</td>
+<td style="text-align:left;">
+▇▁▁▁▁
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-Southampton 031
+WIND_TURBINE_COUNT
 </td>
-<td style="text-align:left;">
-E02003579
+<td style="text-align:right;">
+5546
 </td>
 <td style="text-align:right;">
-13860.94
+0.92
 </td>
 <td style="text-align:right;">
-4460
+0.00
 </td>
 <td style="text-align:right;">
-34052.12
+0.02
 </td>
 <td style="text-align:right;">
-3068
+-1.00
+</td>
+<td style="text-align:right;">
+0.0
+</td>
+<td style="text-align:right;">
+0.00
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+1.00
+</td>
+<td style="text-align:left;">
+▁▁▇▁▁
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-Southampton 021
+TOTAL_FLOOR_AREA
 </td>
-<td style="text-align:left;">
-E02003569
+<td style="text-align:right;">
+0
 </td>
 <td style="text-align:right;">
-13719.22
+1.00
 </td>
 <td style="text-align:right;">
-3999
+73.05
 </td>
 <td style="text-align:right;">
-27661.45
+34.86
 </td>
 <td style="text-align:right;">
-2722
+5.85
+</td>
+<td style="text-align:right;">
+49.0
+</td>
+<td style="text-align:right;">
+69.00
+</td>
+<td style="text-align:right;">
+87
+</td>
+<td style="text-align:right;">
+1353.68
+</td>
+<td style="text-align:left;">
+▇▁▁▁▁
 </td>
 </tr>
-</tbody>
-</table>
-<pre class="r"><code>t2 &lt;- tail(sotonMSOA_DT[, .(MSOA11NM, MSOA11CD, beisElecMWh, nElecMeters,
-                            beisGasMWh, nGasMeters)][order(-beisElecMWh)],5)
-
-kableExtra::kable(t2, caption = &quot;Southampton MSOAs: BEIS 2018 energy data ordered by lowest electricity (bottom 5)&quot;) %&gt;%
-  kable_styling()</code></pre>
-<table class="table" style="margin-left: auto; margin-right: auto;">
-<caption>
-<span id="tab:beisDesc">Table 5.1: </span>Southampton MSOAs: BEIS 2018 energy data ordered by lowest electricity (bottom 5)
-</caption>
-<thead>
-<tr>
-<th style="text-align:left;">
-MSOA11NM
-</th>
-<th style="text-align:left;">
-MSOA11CD
-</th>
-<th style="text-align:right;">
-beisElecMWh
-</th>
-<th style="text-align:right;">
-nElecMeters
-</th>
-<th style="text-align:right;">
-beisGasMWh
-</th>
-<th style="text-align:right;">
-nGasMeters
-</th>
-</tr>
-</thead>
-<tbody>
 <tr>
 <td style="text-align:left;">
-Southampton 024
+epcIsSocialRent
 </td>
-<td style="text-align:left;">
-E02003572
+<td style="text-align:right;">
+0
 </td>
 <td style="text-align:right;">
-9347.893
+1.00
 </td>
 <td style="text-align:right;">
-2597
+0.21
 </td>
 <td style="text-align:right;">
-30332.49
+0.40
 </td>
 <td style="text-align:right;">
-2381
+0.00
+</td>
+<td style="text-align:right;">
+0.0
+</td>
+<td style="text-align:right;">
+0.00
+</td>
+<td style="text-align:right;">
+0
+</td>
+<td style="text-align:right;">
+1.00
+</td>
+<td style="text-align:left;">
+▇▁▁▁▂
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-Southampton 018
+epcIsPrivateRent
 </td>
-<td style="text-align:left;">
-E02003566
+<td style="text-align:right;">
+0
 </td>
 <td style="text-align:right;">
-9221.544
+1.00
 </td>
 <td style="text-align:right;">
-2831
+0.27
 </td>
 <td style="text-align:right;">
-26826.22
+0.44
 </td>
 <td style="text-align:right;">
-2607
+0.00
+</td>
+<td style="text-align:right;">
+0.0
+</td>
+<td style="text-align:right;">
+0.00
+</td>
+<td style="text-align:right;">
+1
+</td>
+<td style="text-align:right;">
+1.00
+</td>
+<td style="text-align:left;">
+▇▁▁▁▃
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-Southampton 008
+epcIsOwnerOcc
 </td>
-<td style="text-align:left;">
-E02003556
+<td style="text-align:right;">
+0
 </td>
 <td style="text-align:right;">
-9199.673
+1.00
 </td>
 <td style="text-align:right;">
-2589
+0.41
 </td>
 <td style="text-align:right;">
-26412.36
+0.49
 </td>
 <td style="text-align:right;">
-2295
+0.00
+</td>
+<td style="text-align:right;">
+0.0
+</td>
+<td style="text-align:right;">
+0.00
+</td>
+<td style="text-align:right;">
+1
+</td>
+<td style="text-align:right;">
+1.00
 </td>
-</tr>
-<tr>
 <td style="text-align:left;">
-Southampton 003
+▇▁▁▁▆
 </td>
+</tr>
+<tr>
 <td style="text-align:left;">
-E02003551
+epcIsUnknownTenure
 </td>
 <td style="text-align:right;">
-8957.742
+0
 </td>
 <td style="text-align:right;">
-2446
+1.00
 </td>
 <td style="text-align:right;">
-17358.87
+0.04
 </td>
 <td style="text-align:right;">
-1649
+0.19
 </td>
-</tr>
-<tr>
-<td style="text-align:left;">
-Southampton 005
-</td>
-<td style="text-align:left;">
-E02003553
+<td style="text-align:right;">
+0.00
 </td>
 <td style="text-align:right;">
-8479.993
+0.0
 </td>
 <td style="text-align:right;">
-2464
+0.00
 </td>
 <td style="text-align:right;">
-24996.91
+0
 </td>
 <td style="text-align:right;">
-2303
+1.00
+</td>
+<td style="text-align:left;">
+▇▁▁▁▁
 </td>
 </tr>
 </tbody>
 </table>
+<p>This leaves us with a total of 71,502 properties.</p>
+<pre class="r"><code>library(stringr)
+finalEPCDT[, POSTCODE_s := stringr::str_remove_all(POSTCODE, &quot; &quot;)]
+sotonPostcodesReducedDT[, POSTCODE_s := stringr::str_remove_all(pcds, &quot; &quot;)]
+setkey(finalEPCDT, POSTCODE_s)
+setkey(sotonPostcodesReducedDT, POSTCODE_s)
+dt &lt;- sotonPostcodesReducedDT[finalEPCDT]
+dt[, MSOACode := msoa11]
+
+setkey(dt, MSOACode)
+setkey(sotonCensus2011_DT, MSOACode)
+
+dt &lt;- sotonCensus2011_DT[dt]
+
+of &lt;- path.expand(&quot;~/data/EW_epc/domestic-E06000045-Southampton/EPCs_liveFinalClean.csv&quot;)
+data.table::fwrite(dt, file = of)
+
+message(&quot;Gziping &quot;, of)</code></pre>
+<pre><code>## Gziping /Users/ben/data/EW_epc/domestic-E06000045-Southampton/EPCs_liveFinalClean.csv</code></pre>
+<pre class="r"><code># Gzip it
+# in case it fails (it will on windows - you will be left with a .csv file)
+try(system( paste0(&quot;gzip -f &#39;&quot;, of,&quot;&#39;&quot;))) # include &#39; or it breaks on spaces
+message(&quot;Gzipped &quot;, of)</code></pre>
+<pre><code>## Gzipped /Users/ben/data/EW_epc/domestic-E06000045-Southampton/EPCs_liveFinalClean.csv</code></pre>
+<p>NB: this failed to match an EPC postcode to an MSOA for 72 EPCs The table below shows which postcodes these were by date.</p>
+<pre class="r"><code>dt[is.na(MSOACode), .(nEPCs = .N), keyby = .(POSTCODE_s, TENURE, INSPECTION_DATE)]</code></pre>
+<pre><code>##     POSTCODE_s           TENURE INSPECTION_DATE nEPCs
+##  1:    SO156GB          unknown      2015-04-08     3
+##  2:    SO156GB          unknown      2015-06-09     1
+##  3:    SO156GB          unknown      2015-07-06    24
+##  4:    SO156GB          unknown      2015-08-04    12
+##  5:    SO156GB          unknown      2015-08-14     9
+##  6:    SO156GB          unknown      2015-08-19     1
+##  7:    SO160AL                       2009-02-17     4
+##  8:    SO160AL rental (private)      2019-11-20     2
+##  9:    SO162AJ          unknown      2018-03-22     2
+## 10:    SO167HE   owner-occupied      2009-11-30     1
+## 11:    SO167HE   owner-occupied      2017-10-04     1
+## 12:    SO168AD                       2011-03-18     1
+## 13:    SO168AD   owner-occupied      2008-10-01     1
+## 14:    SO168AD   owner-occupied      2009-09-29     1
+## 15:    SO168AD   owner-occupied      2019-04-08     1
+## 16:    SO168AD   owner-occupied      2020-06-17     1
+## 17:    SO181HS rental (private)      2010-04-27     1
+## 18:    SO185BR   owner-occupied      2018-06-05     1
+## 19:    SO185BS   owner-occupied      2012-02-15     1
+## 20:    SO185BS   owner-occupied      2018-03-14     1
+## 21:    SO196AQ          unknown      2014-11-05     3
+##     POSTCODE_s           TENURE INSPECTION_DATE nEPCs</code></pre>
 </div>
-<div id="save-msoa-aggregates-for-re-use" class="section level1">
-<h1><span class="header-section-number">6</span> Save MSOA aggregates for re-use</h1>
+<div id="summarise-and-save-msoa-aggregates-for-re-use" class="section level1">
+<h1><span class="header-section-number">5</span> Summarise and save MSOA aggregates for re-use</h1>
 <p>Finally we save the MSOA table into the repo data directory for future use. We don’t usually advocate keeping data in a git repo but this is small, aggregated and <a href="https://en.wikipedia.org/wiki/Mostly_Harmless">mostly harmless</a>.</p>
 <pre class="r"><code>of &lt;-  here::here(&quot;data&quot;, &quot;sotonMSOAdata.csv&quot;)
 
@@ -5354,7 +5609,7 @@ message(&quot;Saved &quot;, nrow(sotonMSOA_DT), &quot; rows of data.&quot;)</cod
 <pre><code>## Saved 32 rows of data.</code></pre>
 </div>
 <div id="r-packages-used" class="section level1">
-<h1><span class="header-section-number">7</span> R packages used</h1>
+<h1><span class="header-section-number">6</span> R packages used</h1>
 <ul>
 <li>rmarkdown <span class="citation">(Allaire et al. 2018)</span></li>
 <li>bookdown <span class="citation">(Xie 2016a)</span></li>
@@ -5366,14 +5621,14 @@ message(&quot;Saved &quot;, nrow(sotonMSOA_DT), &quot; rows of data.&quot;)</cod
 </ul>
 </div>
 <div id="annex" class="section level1">
-<h1><span class="header-section-number">8</span> Annex</h1>
+<h1><span class="header-section-number">7</span> Annex</h1>
 <div id="tables" class="section level2">
-<h2><span class="header-section-number">8.1</span> Tables</h2>
+<h2><span class="header-section-number">7.1</span> Tables</h2>
 <pre class="r"><code>kableExtra::kable(kt1[order(-pc_missingHH)], digits = 2, caption = &quot;EPC records as a % of n census households and n meters per MSOA&quot;) %&gt;%
   kable_styling()</code></pre>
 <table class="table" style="margin-left: auto; margin-right: auto;">
 <caption>
-<span id="tab:bigMSOATable">Table 8.1: </span>EPC records as a % of n census households and n meters per MSOA
+<span id="tab:bigMSOATable">Table 7.1: </span>EPC records as a % of n census households and n meters per MSOA
 </caption>
 <thead>
 <tr>
@@ -5436,7 +5691,7 @@ E02003577
 6734
 </td>
 <td style="text-align:right;">
-5879
+5917
 </td>
 <td style="text-align:right;">
 37.92
@@ -5451,19 +5706,19 @@ E02003577
 24.67
 </td>
 <td style="text-align:right;">
-77245.24
+77383.23
 </td>
 <td style="text-align:right;">
 47461.33
 </td>
 <td style="text-align:right;">
-119.8
+120.6
 </td>
 <td style="text-align:right;">
-87.3
+87.9
 </td>
 <td style="text-align:right;">
-162.8
+163.0
 </td>
 </tr>
 <tr>
@@ -5512,90 +5767,90 @@ E02003571
 </tr>
 <tr>
 <td style="text-align:left;">
-Southampton 022
+Southampton 017
 </td>
 <td style="text-align:left;">
-E02003570
+E02003565
 </td>
 <td style="text-align:right;">
-3635
+2563
 </td>
 <td style="text-align:right;">
-4142
+2840
 </td>
 <td style="text-align:right;">
-3424
+2427
 </td>
 <td style="text-align:right;">
-25.56
+48.81
 </td>
 <td style="text-align:right;">
-29.82
+11.63
 </td>
 <td style="text-align:right;">
-45.69
+57.24
 </td>
 <td style="text-align:right;">
-22.59
+28.95
 </td>
 <td style="text-align:right;">
-58326.68
+42637.78
 </td>
 <td style="text-align:right;">
-49449.97
+35191.63
 </td>
 <td style="text-align:right;">
-94.2
+94.7
 </td>
 <td style="text-align:right;">
-82.7
+85.5
 </td>
 <td style="text-align:right;">
-118.0
+121.2
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-Southampton 017
+Southampton 022
 </td>
 <td style="text-align:left;">
-E02003565
+E02003570
 </td>
 <td style="text-align:right;">
-2563
+3635
 </td>
 <td style="text-align:right;">
-2840
+4142
 </td>
 <td style="text-align:right;">
-2397
+3429
 </td>
 <td style="text-align:right;">
-48.81
+25.56
 </td>
 <td style="text-align:right;">
-11.63
+29.82
 </td>
 <td style="text-align:right;">
-57.24
+45.69
 </td>
 <td style="text-align:right;">
-28.95
+22.59
 </td>
 <td style="text-align:right;">
-42543.37
+58338.87
 </td>
 <td style="text-align:right;">
-35191.63
+49449.97
 </td>
 <td style="text-align:right;">
-93.5
+94.3
 </td>
 <td style="text-align:right;">
-84.4
+82.8
 </td>
 <td style="text-align:right;">
-120.9
+118.0
 </td>
 </tr>
 <tr>
@@ -5612,7 +5867,7 @@ E02003579
 4460
 </td>
 <td style="text-align:right;">
-3112
+3137
 </td>
 <td style="text-align:right;">
 44.92
@@ -5627,19 +5882,19 @@ E02003579
 63.09
 </td>
 <td style="text-align:right;">
-46140.81
+46309.49
 </td>
 <td style="text-align:right;">
 47913.06
 </td>
 <td style="text-align:right;">
-92.7
+93.4
 </td>
 <td style="text-align:right;">
-69.8
+70.3
 </td>
 <td style="text-align:right;">
-96.3
+96.7
 </td>
 </tr>
 <tr>
@@ -5688,6 +5943,50 @@ E02003561
 </tr>
 <tr>
 <td style="text-align:left;">
+Southampton 021
+</td>
+<td style="text-align:left;">
+E02003569
+</td>
+<td style="text-align:right;">
+3527
+</td>
+<td style="text-align:right;">
+3999
+</td>
+<td style="text-align:right;">
+2754
+</td>
+<td style="text-align:right;">
+40.71
+</td>
+<td style="text-align:right;">
+15.00
+</td>
+<td style="text-align:right;">
+38.28
+</td>
+<td style="text-align:right;">
+44.32
+</td>
+<td style="text-align:right;">
+47718.63
+</td>
+<td style="text-align:right;">
+41380.67
+</td>
+<td style="text-align:right;">
+78.1
+</td>
+<td style="text-align:right;">
+68.9
+</td>
+<td style="text-align:right;">
+115.3
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
 Southampton 009
 </td>
 <td style="text-align:left;">
@@ -5744,7 +6043,7 @@ E02003568
 3900
 </td>
 <td style="text-align:right;">
-2954
+2959
 </td>
 <td style="text-align:right;">
 50.08
@@ -5765,10 +6064,10 @@ E02003568
 47024.09
 </td>
 <td style="text-align:right;">
-77.3
+77.5
 </td>
 <td style="text-align:right;">
-75.7
+75.9
 </td>
 <td style="text-align:right;">
 113.7
@@ -5788,7 +6087,7 @@ E02003558
 3222
 </td>
 <td style="text-align:right;">
-2216
+2223
 </td>
 <td style="text-align:right;">
 33.96
@@ -5803,16 +6102,16 @@ E02003558
 39.81
 </td>
 <td style="text-align:right;">
-38568.39
+38565.55
 </td>
 <td style="text-align:right;">
 34421.99
 </td>
 <td style="text-align:right;">
-75.8
+76.0
 </td>
 <td style="text-align:right;">
-68.8
+69.0
 </td>
 <td style="text-align:right;">
 112.0
@@ -5820,50 +6119,6 @@ E02003558
 </tr>
 <tr>
 <td style="text-align:left;">
-Southampton 021
-</td>
-<td style="text-align:left;">
-E02003569
-</td>
-<td style="text-align:right;">
-3527
-</td>
-<td style="text-align:right;">
-3999
-</td>
-<td style="text-align:right;">
-2671
-</td>
-<td style="text-align:right;">
-40.71
-</td>
-<td style="text-align:right;">
-15.00
-</td>
-<td style="text-align:right;">
-38.28
-</td>
-<td style="text-align:right;">
-44.32
-</td>
-<td style="text-align:right;">
-46831.10
-</td>
-<td style="text-align:right;">
-41380.67
-</td>
-<td style="text-align:right;">
-75.7
-</td>
-<td style="text-align:right;">
-66.8
-</td>
-<td style="text-align:right;">
-113.2
-</td>
-</tr>
-<tr>
-<td style="text-align:left;">
 Southampton 015
 </td>
 <td style="text-align:left;">
@@ -5876,7 +6131,7 @@ E02003563
 3818
 </td>
 <td style="text-align:right;">
-2545
+2553
 </td>
 <td style="text-align:right;">
 37.81
@@ -5891,16 +6146,16 @@ E02003563
 51.79
 </td>
 <td style="text-align:right;">
-46490.24
+46524.57
 </td>
 <td style="text-align:right;">
 39920.85
 </td>
 <td style="text-align:right;">
-73.1
+73.3
 </td>
 <td style="text-align:right;">
-66.7
+66.9
 </td>
 <td style="text-align:right;">
 116.5
@@ -5964,7 +6219,7 @@ E02003555
 3763
 </td>
 <td style="text-align:right;">
-2258
+2261
 </td>
 <td style="text-align:right;">
 34.59
@@ -5979,19 +6234,19 @@ E02003555
 56.15
 </td>
 <td style="text-align:right;">
-35186.09
+35181.90
 </td>
 <td style="text-align:right;">
 40416.83
 </td>
 <td style="text-align:right;">
-71.9
+72.0
 </td>
 <td style="text-align:right;">
-60.0
+60.1
 </td>
 <td style="text-align:right;">
-87.1
+87.0
 </td>
 </tr>
 <tr>
@@ -6008,7 +6263,7 @@ E02003553
 2464
 </td>
 <td style="text-align:right;">
-1686
+1687
 </td>
 <td style="text-align:right;">
 39.01
@@ -6023,19 +6278,19 @@ E02003553
 32.00
 </td>
 <td style="text-align:right;">
-31930.49
+31991.63
 </td>
 <td style="text-align:right;">
 33476.91
 </td>
 <td style="text-align:right;">
-70.4
+70.5
 </td>
 <td style="text-align:right;">
-68.4
+68.5
 </td>
 <td style="text-align:right;">
-95.4
+95.6
 </td>
 </tr>
 <tr>
@@ -6052,7 +6307,7 @@ E02003562
 3921
 </td>
 <td style="text-align:right;">
-2489
+2513
 </td>
 <td style="text-align:right;">
 45.68
@@ -6073,10 +6328,10 @@ E02003562
 51289.66
 </td>
 <td style="text-align:right;">
-68.5
+69.1
 </td>
 <td style="text-align:right;">
-63.5
+64.1
 </td>
 <td style="text-align:right;">
 91.9
@@ -6096,7 +6351,7 @@ E02003580
 2825
 </td>
 <td style="text-align:right;">
-1783
+1804
 </td>
 <td style="text-align:right;">
 27.21
@@ -6111,19 +6366,19 @@ E02003580
 35.69
 </td>
 <td style="text-align:right;">
-30726.44
+31120.78
 </td>
 <td style="text-align:right;">
 24488.16
 </td>
 <td style="text-align:right;">
-68.1
+68.9
 </td>
 <td style="text-align:right;">
-63.1
+63.9
 </td>
 <td style="text-align:right;">
-125.5
+127.1
 </td>
 </tr>
 <tr>
@@ -6172,90 +6427,90 @@ E02003573
 </tr>
 <tr>
 <td style="text-align:left;">
-Southampton 012
+Southampton 003
 </td>
 <td style="text-align:left;">
-E02003560
+E02003551
 </td>
 <td style="text-align:right;">
-3040
+2256
 </td>
 <td style="text-align:right;">
-3191
+2446
 </td>
 <td style="text-align:right;">
-1952
+1456
 </td>
 <td style="text-align:right;">
-26.97
+33.69
 </td>
 <td style="text-align:right;">
-53.52
+38.96
 </td>
 <td style="text-align:right;">
-8.75
+15.29
 </td>
 <td style="text-align:right;">
-36.12
+42.95
 </td>
 <td style="text-align:right;">
-33862.76
+27395.69
 </td>
 <td style="text-align:right;">
-34252.94
+26316.61
 </td>
 <td style="text-align:right;">
-64.2
+64.5
 </td>
 <td style="text-align:right;">
-61.2
+59.5
 </td>
 <td style="text-align:right;">
-98.9
+104.1
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-Southampton 003
+Southampton 012
 </td>
 <td style="text-align:left;">
-E02003551
+E02003560
 </td>
 <td style="text-align:right;">
-2256
+3040
 </td>
 <td style="text-align:right;">
-2446
+3191
 </td>
 <td style="text-align:right;">
-1445
+1952
 </td>
 <td style="text-align:right;">
-33.69
+26.97
 </td>
 <td style="text-align:right;">
-38.96
+53.52
 </td>
 <td style="text-align:right;">
-15.29
+8.75
 </td>
 <td style="text-align:right;">
-42.95
+36.12
 </td>
 <td style="text-align:right;">
-27436.58
+33862.76
 </td>
 <td style="text-align:right;">
-26316.61
+34252.94
 </td>
 <td style="text-align:right;">
-64.1
+64.2
 </td>
 <td style="text-align:right;">
-59.1
+61.2
 </td>
 <td style="text-align:right;">
-104.3
+98.9
 </td>
 </tr>
 <tr>
@@ -6272,7 +6527,7 @@ E02003554
 2873
 </td>
 <td style="text-align:right;">
-1683
+1684
 </td>
 <td style="text-align:right;">
 46.49
@@ -6287,7 +6542,7 @@ E02003554
 63.00
 </td>
 <td style="text-align:right;">
-35573.16
+35570.20
 </td>
 <td style="text-align:right;">
 39712.77
@@ -6348,46 +6603,46 @@ E02003552
 </tr>
 <tr>
 <td style="text-align:left;">
-Southampton 028
+Southampton 016
 </td>
 <td style="text-align:left;">
-E02003576
+E02003564
 </td>
 <td style="text-align:right;">
-3434
+3474
 </td>
 <td style="text-align:right;">
-3614
+3563
 </td>
 <td style="text-align:right;">
-2120
+2164
 </td>
 <td style="text-align:right;">
-38.99
+39.38
 </td>
 <td style="text-align:right;">
-22.83
+22.54
 </td>
 <td style="text-align:right;">
-18.58
+12.09
 </td>
 <td style="text-align:right;">
-56.41
+63.39
 </td>
 <td style="text-align:right;">
-39556.61
+43679.63
 </td>
 <td style="text-align:right;">
-44100.48
+43718.49
 </td>
 <td style="text-align:right;">
-61.7
+62.3
 </td>
 <td style="text-align:right;">
-58.7
+60.7
 </td>
 <td style="text-align:right;">
-89.7
+99.9
 </td>
 </tr>
 <tr>
@@ -6404,7 +6659,7 @@ E02003566
 2831
 </td>
 <td style="text-align:right;">
-1600
+1617
 </td>
 <td style="text-align:right;">
 35.21
@@ -6419,63 +6674,63 @@ E02003566
 59.42
 </td>
 <td style="text-align:right;">
-27067.98
+27441.65
 </td>
 <td style="text-align:right;">
 36047.76
 </td>
 <td style="text-align:right;">
-61.4
+62.0
 </td>
 <td style="text-align:right;">
-56.5
+57.1
 </td>
 <td style="text-align:right;">
-75.1
+76.1
 </td>
 </tr>
 <tr>
 <td style="text-align:left;">
-Southampton 016
+Southampton 028
 </td>
 <td style="text-align:left;">
-E02003564
+E02003576
 </td>
 <td style="text-align:right;">
-3474
+3434
 </td>
 <td style="text-align:right;">
-3563
+3614
 </td>
 <td style="text-align:right;">
-2124
+2121
 </td>
 <td style="text-align:right;">
-39.38
+38.99
 </td>
 <td style="text-align:right;">
-22.54
+22.83
 </td>
 <td style="text-align:right;">
-12.09
+18.58
 </td>
 <td style="text-align:right;">
-63.39
+56.41
 </td>
 <td style="text-align:right;">
-42655.74
+39556.61
 </td>
 <td style="text-align:right;">
-43718.49
+44100.48
 </td>
 <td style="text-align:right;">
-61.1
+61.8
 </td>
 <td style="text-align:right;">
-59.6
+58.7
 </td>
 <td style="text-align:right;">
-97.6
+89.7
 </td>
 </tr>
 <tr>
@@ -6536,7 +6791,7 @@ E02003550
 3527
 </td>
 <td style="text-align:right;">
-1923
+1932
 </td>
 <td style="text-align:right;">
 43.10
@@ -6551,19 +6806,19 @@ E02003550
 66.08
 </td>
 <td style="text-align:right;">
-36493.87
+36605.55
 </td>
 <td style="text-align:right;">
 41124.17
 </td>
 <td style="text-align:right;">
-59.8
+60.1
 </td>
 <td style="text-align:right;">
-54.5
+54.8
 </td>
 <td style="text-align:right;">
-88.7
+89.0
 </td>
 </tr>
 <tr>
@@ -6624,7 +6879,7 @@ E02003574
 3599
 </td>
 <td style="text-align:right;">
-1971
+1972
 </td>
 <td style="text-align:right;">
 40.77
@@ -6800,7 +7055,7 @@ E02003556
 2589
 </td>
 <td style="text-align:right;">
-1321
+1339
 </td>
 <td style="text-align:right;">
 42.57
@@ -6815,19 +7070,19 @@ E02003556
 70.42
 </td>
 <td style="text-align:right;">
-27501.24
+28093.93
 </td>
 <td style="text-align:right;">
 35612.03
 </td>
 <td style="text-align:right;">
-53.5
+54.2
 </td>
 <td style="text-align:right;">
-51.0
+51.7
 </td>
 <td style="text-align:right;">
-77.2
+78.9
 </td>
 </tr>
 </tbody>
-- 
GitLab