1 Problem

Identify locations outside the Industrial Land Designation areas with high concentration of manufacturing, other economic activities and residences.

2 Context

We have analysed 3 open data datasets refering to Manufacturing in London.

Name Source Observation General_Desc Version
Directory of London Businesses - UK Standard Industrial Classification (SIC) London Data Store (1) “Data snapshot containing basic company data of live companies on the register. Each entry represents a financial accounts submission of either a whole company or part of one. Some businesses have more than one entry in the directory because they need to submit more than one set of accounts for different parts of their business.” “The London cut available to download here, was created using a postcode list… inaccuracies in postcodes may mean that no local authority is listed for a company.” Point data, N = 1,130,474 obs. May 2018
Business Register and Employment Survey - BRES NOMIS - Official Labour Market Statistics ONS (2) “An employer survey of the number of jobs held by employees broken down by full/part-time and detailed industry (5 digit SIC2007)” Estimated number of jobs per LSOA (N = 4,835 obs.) Includes Industry percentage (percentage of jobs in Manufacturing) (3) 2017 (latest data)
Inter Departmental Business Register (IDBR) NOMIS - Official Labour Market Statistics ONS “An extract recording the number of local units that were live at a reference date in March. Estimates can be broken down by employment size band, detailed industry (5 digit SIC2007) and legal status.” - “A local unit is an individual site (for example a factory or shop) associated with an enterprise. It can also be referred to as a workplace.” Number of workplaces per MSOA (N = 983 obs.) (4) 2018 (latest data)

URLs:
1 https://data.london.gov.uk
2 https://www.nomisweb.co.uk/
3 https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/bulletins/businessregisterandemploymentsurveybresprovisionalresults/provisionalresults2017revisedresults2016#quality-and-methodology
4 https://www.ons.gov.uk/aboutus/whatwedo/paidservices/interdepartmentalbusinessregisteridbr https://www.ons.gov.uk/businessindustryandtrade/business/activitysizeandlocation/methodologies/ukbusinessactivitysizeandlocationqmi

3 Cartographic summary

London Businesses Directory
https://npalomin.github.io/pnum/fame_rev.html

Manufacturing Jobs percentage per LSOA
https://github.com/npalomin/pnum/blob/master/ss_lsoa_light.md

Local Units and Enterprises Count per LSOA
https://npalomin.github.io/pnum/IDBR.html

GLA-Designated Industrial Land
https://npalomin.github.io/pnum/GLA_indLand/GLA_indLand.html

4 Analysis of BRES

A detailed analysis was carried out for the BRES dataset due to four reasons:

  1. The number of manufacturing jobs obtained from the BRES dataset is considered the best proxy to represent manufacturing intensity. This measure can be interpreted as a combination of number of manufacturing units and size.

  2. The level of detail of the BRES dataset at the LSOA area is considered sufficient to allow the selection of case study areas for micro-morphology analysis.

  3. The SIC dataset despite having a finer-grain resolution has shown innacuracies in the location of the data points (reference: Vyner street case study Cites of Making project).

  4. Despite the fact that the IDBR considers a larger sample at the national level, it is aggregated at a lower resolution geographic area (MSOA). Also, because according to a data methodological note “it is possible to get multiple business registrations at a single address and this can distort data for smaller geographical areas” (https://www.ons.gov.uk/businessindustryandtrade/business/activitysizeandlocation/methodologies/ukbusinessactivitysizeandlocationqmi/pdf).

4.1 Metadata

Data source: NOMIS official labour market statistics (ONS) URL: https://www.nomisweb.co.uk/
Definition: “An employer survey of the number of jobs held by employees broken down by full/part-time and detailed industry (5 digit SIC2007). The survey records a job at the location of an employees workplace. Available from country down to lower level super output area and Scottish datazone.”
Ref URLs: https://onsdigital.github.io/dp-classification-tools/standard-industrial-classification/ONS_SIC_hierarchy_view.html

Read NOMIS data. Lower Layer Super Output Area (2011)
Industry Standard industrial classification of economic activities (SIC) 2007 section C: Manufacturing

nom <- read_csv("/Volumes/ritd-ag-project-rd00lq-jamfe87/GIS_Analysis/dataProcessed/nomis_4835.csv") # 4,835 obs.

4.2 Data summary

Create variable ‘lsoa11_cd’ from variable ‘lsoa11_2011’ substract chr 1 to 4 (first 4 characters)

nom <- mutate(nom, lsoa11_cd = substr(lsoa_2011, 1, 9))

Summary of ‘nom’ Count and Industry percentage

summary(nom)
##   lsoa_2011             Count         Industry percentage
##  Length:4835        Min.   :   0.00   Min.   :  0.000    
##  Class :character   1st Qu.:   0.00   1st Qu.:  0.000    
##  Mode  :character   Median :   0.00   Median :  0.000    
##                     Mean   :  22.12   Mean   :  1.814    
##                     3rd Qu.:  10.00   3rd Qu.:  1.400    
##                     Max.   :6000.00   Max.   :100.000    
##   lsoa11_cd        
##  Length:4835       
##  Class :character  
##  Mode  :character  
##                    
##                    
## 
## [1] 34
Jobs count frequency per LSOA (N = 4,835)
Jobs Frequency Jobs Frequency Jobs Frequency Jobs Frequency
0 3052 50 72 350 9 1750 1
5 366 75 51 400 11 3000 1
10 539 100 53 450 2 4500 1
15 120 125 18 500 3 6000 1
20 151 150 34 600 11
25 60 175 8 700 6
30 75 200 14 800 4
35 33 225 6 900 4
40 57 250 21 1000 5
45 28 300 16 1500 2

Most LSOAs have 0 jobs in manufacturing. Among the rest the most frequent number is 10 jobs (in 539 LSOAs). High number of jobs ( > 1500) are an exception.

## [1] 107
Jobs Percentage frequency per LSOA (N = 4,835)
Perc. Frequency Perc. Frequency Perc. Frequency Perc. Frequency Perc. Frequency
0 3105 2.2 27 5.7 12 12.5 16 26.7 7
0.1 17 2.3 7 5.8 5 12.9 2 27.8 1
0.2 51 2.4 1 6 14 13.3 12 28 1
0.3 47 2.5 82 6.2 9 13.9 2 28.6 1
0.4 42 2.7 5 6.4 2 14 1 30 6
0.5 38 2.8 5 6.7 81 14.3 3 33.3 9
0.6 45 2.9 45 7 3 15 12 34.3 1
0.7 41 3 25 7.1 4 16 2 35 1
0.8 48 3.1 3 7.5 16 16.7 18 36 1
0.9 13 3.2 2 8 26 17.5 1 37.5 1
1 60 3.3 96 8.3 16 17.8 1 40 3
1.1 31 3.5 3 8.6 4 18 2 41.7 2
1.2 72 3.6 13 8.8 3 18.8 7 50 4
1.3 11 3.8 13 8.9 1 20 22 53.3 1
1.4 42 3.9 4 9.4 1 20.8 1 60 2
1.5 12 4 56 10 69 21.4 1 66.7 1
1.6 2 4.2 3 10.7 2 22.2 2 70 1
1.7 86 4.3 10 11.1 3 22.9 1 75 1
1.8 3 4.4 12 11.2 4 23.3 1 100 1
1.9 12 4.5 3 11.4 2 23.7 1
2 71 5 102 11.7 3 24 3
2.1 11 5.6 7 12 9 25 6

Most LSOAs have 0% jobs in manufacturing. Among the rest the most frequent percentage is 5% (in 102 LSOAs). High percentage of jobs ( > 53%) are an exception.

4.3 Histograms

# drop 0 values
nom1 <- subset(nom, nom$Count > 0 & nom$`Industry percentage` > 0) # 1730

Summary of values > 0

summary(nom1)
##   lsoa_2011             Count         Industry percentage
##  Length:1730        Min.   :   5.00   Min.   :  0.100    
##  Class :character   1st Qu.:  10.00   1st Qu.:  1.125    
##  Mode  :character   Median :  15.00   Median :  2.500    
##                     Mean   :  61.51   Mean   :  5.070    
##                     3rd Qu.:  40.00   3rd Qu.:  6.000    
##                     Max.   :6000.00   Max.   :100.000    
##   lsoa11_cd        
##  Length:1730       
##  Class :character  
##  Mode  :character  
##                    
##                    
## 
## Warning: Removed 126 rows containing non-finite values (stat_bin).
## Warning: Removed 2 rows containing missing values (geom_bar).
## Warning: Removed 18 rows containing non-finite values (stat_bin).
## Warning: Removed 2 rows containing missing values (geom_bar).

4.4 Boxpolots

4.5 Maps

lsoa <- st_read("/Volumes/ritd-ag-project-rd00lq-jamfe87/GIS_Analysis/dataRaw/statistical-gis-boundaries-london/ESRI/LSOA_2011_London_gen_MHW.shp")
## Reading layer `LSOA_2011_London_gen_MHW' from data source `/Volumes/ritd-ag-project-rd00lq-jamfe87/GIS_Analysis/dataRaw/statistical-gis-boundaries-london/ESRI/LSOA_2011_London_gen_MHW.shp' using driver `ESRI Shapefile'
## Simple feature collection with 4835 features and 15 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 503574.2 ymin: 155850.8 xmax: 561956.7 ymax: 200933.6
## epsg (SRID):    NA
## proj4string:    +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.999601272 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defs

Create gray London basemap

Join with NOMIS data ‘nom’

mpl <- append_data(lsoa, nom, key.shp = "LSOA11CD", key.data = "lsoa11_cd", ignore.duplicates = TRUE, ignore.na = TRUE)
## Keys match perfectly.
names(mpl)
##  [1] "LSOA11CD"            "LSOA11NM"            "MSOA11CD"           
##  [4] "MSOA11NM"            "LAD11CD"             "LAD11NM"            
##  [7] "RGN11CD"             "RGN11NM"             "USUALRES"           
## [10] "HHOLDRES"            "COMESTRES"           "POPDEN"             
## [13] "HHOLDS"              "AVHHOLDSZ"           "LSOAa"              
## [16] "lsoa_2011"           "Count"               "Industry percentage"
## [19] "geometry"

Maps of job Counts > 40 and Percentage > 6% (> 3rd Q)

A visual comparison shows that LSOAs with high number of jobs in some cases is coincident with LSOAs with high percentage of jobs. Both meaures might be indicators of ‘manufacturing intensity’.

Interactive Map
https://npalomin.github.io/pnum/msum.html

#msum.i <- msum
#tmap_mode("view")
#msum.i
#tmap_save(msum.i, "msum.html")

The selection of values with both conditions Count >= 40 & IndPer >= 6 shows 253 obs.

5 Industrial Land 2015

ild <- st_read("/Volumes/ritd-ag-project-rd00lq-jamfe87/GIS_Analysis/dataProcessed/GLA_Ind_Land_Baseline_2015/GLA_Ind_Land_bl_2015.shp")
## Reading layer `GLA_Ind_Land_bl_2015' from data source `/Volumes/ritd-ag-project-rd00lq-jamfe87/GIS_Analysis/dataProcessed/GLA_Ind_Land_Baseline_2015/GLA_Ind_Land_bl_2015.shp' using driver `ESRI Shapefile'
## Simple feature collection with 104006 features and 15 fields
## geometry type:  MULTIPOLYGON
## dimension:      XYZ
## bbox:           xmin: 503943.6 ymin: 157513.4 xmax: 561088.1 ymax: 200761.6
## epsg (SRID):    NA
## proj4string:    +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defs

Summary of ‘ild’ (n=104006 obs.)

summary(ild)
##     OBJECTID        OBJECTID_1       LU_CODE_20           BOROUGH     
##  Min.   :     1   Min.   :  5875   Min.   : 0.000   Ealing    :10494  
##  1st Qu.: 26002   1st Qu.: 39820   1st Qu.: 2.000   Hillingdon: 9114  
##  Median : 52004   Median : 72514   Median : 5.000   Hounslow  : 8881  
##  Mean   : 52004   Mean   : 75011   Mean   : 6.917   Brent     : 8679  
##  3rd Qu.: 78005   3rd Qu.:118445   3rd Qu.: 9.000   Enfield   : 6575  
##  Max.   :104006   Max.   :146437   Max.   :25.000   Sutton    : 4541  
##                                                     (Other)   :55722  
##      URSID      
##  2045   : 1211  
##  SIL58  : 1201  
##  SIL53.3: 1200  
##  SIL99.2:  999  
##  2096   :  960  
##  (Other):71563  
##  NA's   :26872  
##                                                  Note         LU_Code_21  
##  1                                                 : 1560   Min.   : 1.0  
##  Docks data unchanged from 2010 data               : 1200   1st Qu.: 2.0  
##  Council Update 2015                               :  924   Median : 3.0  
##  No update info updated sperately from 2015 exerci*:  762   Mean   : 6.1  
##  Source UK Map                                     :  168   3rd Qu.: 8.0  
##  (Other)                                           :  249   Max.   :24.0  
##  NA's                                              :99143                 
##  TYPE_2015    GISUPDATE_      Area_Ha           Shape_Leng      
##  LSIS:20264   N/A :94299   Min.   : 0.00000   Min.   :    0.00  
##  NAL :45716   new :  300   1st Qu.: 0.00182   1st Qu.:   19.46  
##  SIL :38026   WA  :   69   Median : 0.00674   Median :   45.30  
##               WAWU:   90   Mean   : 0.07296   Mean   :  109.15  
##               WU  : 9248   3rd Qu.: 0.03334   3rd Qu.:  114.88  
##                            Max.   :81.45079   Max.   :20927.35  
##                                                                 
##     Easting          Northing        Shape_Le_1         Shape_Area      
##  Min.   :     0   Min.   :     0   Min.   :    0.00   Min.   :     0.0  
##  1st Qu.:517967   1st Qu.:174716   1st Qu.:   19.46   1st Qu.:    18.2  
##  Median :526235   Median :180326   Median :   45.30   Median :    67.4  
##  Mean   :525519   Mean   :179624   Mean   :  109.15   Mean   :   729.6  
##  3rd Qu.:535057   3rd Qu.:185239   3rd Qu.:  114.88   3rd Qu.:   333.4  
##  Max.   :561042   Max.   :200758   Max.   :20927.35   Max.   :814507.9  
##                                                                         
##            geometry     
##  MULTIPOLYGON Z:104006  
##  epsg:NA       :     0  
##  +proj=tmer... :     0  
##                         
##                         
##                         
##

Largest area is near 81 ha and 1st Qu goes from 0 to 18 sqm. (sqm to ha = divide by 10k) Frequency by type (‘TYPE_2015’)

Map of GLA Designated Industrial Land
https://npalomin.github.io/pnum/GLA_indLand/GLA_indLand.html

Shape area summary by Type

# library(purrr)
# typeSUM <- ild %>% split(.$TYPE_2015) %>% map(summary)
Industrial Land Type
TYPE_2015 mean median min max
LSIS 570.92 86.47 0 165982.37
NAL 555.58 42.68 0 359925.45
SIL 1023.27 99.16 0 814507.91

Analyse area distributuon by type

Industrial Land Type
(-Inf,5] (5,30] (30,100] (100,500] (500,5000] (5000,20000] (20000, Inf]
LSIS 1235 4981 4482 5615 3576 327 48
NAL 2755 16870 9381 9328 6559 674 149
SIL 2042 9432 7594 10001 7705 1005 247
Total 6032 31283 21457 24944 17840 2006 444
Industrial Land Type Area Distribution (natural breaks)
(0,2762] (2762,13001] (13001,38525] (38525,100191] (100191,217698] (217698,359925] (359925,814508]
LSIS 19489 679 81 12 3 0 0
NAL 43959 1490 215 40 6 1 1
SIL 35663 1938 316 81 21 3 3
Total 99111 4107 612 133 30 4 4

Sample of “URSID” = ‘’ (N=26,876 obs.) by ‘TYPE_2015’

26,640 obs. (out of N=26,876 obs.) are ‘NAL’ ‘TYPE_2015’ (see AECOM map for meaning of NAL - might be Non-designated Sites, eventhough they are part of the ‘GLA-Desiganted Industrial Land’ dataset). The absence of ‘URSID’ code might be related to being ‘Non-designated Sites’. However, there’re ‘big’ sites of ‘LSIS’ and ‘SIL’ type without URSID code. Also, there might be uncoded ‘NAL’ type areas that have the potential of being classified as ‘LSIS’ or ‘SIL’. The size of the area might be a factor to consider.
How is the size of the uncoded sites distributed according to ‘TYPE_2015’?

##      (-Inf,5] (5,30] (30,100] (100,500] (500,5000] (5000,20000]
## LSIS       10     20       29        57        121           22
## NAL       942  11740     5296      4547       3647          239
## SIL        18     33       20        24         36           25
##      (20000, Inf]
## LSIS            9
## NAL            29
## SIL             8

In QGIS join attributes by location to calculate ‘Shape_Area’ of Industrial Land Designation per LSOA.

library(readr)
ildp <- read_csv("/Volumes/ritd-ag-project-rd00lq-jamfe87/GIS_Analysis/dataProcessed/GLA_Ind_Land_Baseline_2015/ildp.csv") # 4483

Map of LSOA by ‘Shape_Area’ (‘x’ in dataset) 

c_breaks <- getJenksBreaks(mpl_il$sh_a, 6)
c_breaks <- append(c_breaks, c(20, 1000))
c_breaks <- sort(c_breaks)

Map of Industrial Land highlighting LSOAs with Manufacturing Jobs Count and Percentage > 3rd QU

Plot

Interactive Map
https://npalomin.github.io/pnum/mH.html

# mH.i <- mH
# tmap_mode("view")
# mH.i
# tmap_mode("plot")
# tmap_save(mH.i, "mH.html")

Selection of values by Conditions Count >= 40 & IndPer >= 6 & sh_a < 1000

Interactive Map https://npalomin.github.io/pnum/mC3.html

# mC3.i <- mC3
# tmap_mode("view")
# mC3.i
# tmap_mode("plot")
# tmap_save(mC3.i, "mC3.html")

In QGIS join attributes by location to calculate ‘Shape_Area’ of Industrial Land Designation per LSOA.

c_sum <- read_csv("/Volumes/ritd-ag-project-rd00lq-jamfe87/GIS_Analysis/dataProcessed/GLA_Ind_Land_Baseline_2015/c_sum.csv")

Maps of Industrial Land Designation (ILD) by ‘TYPE_2015’ per LSOA

wide_DF <- read_csv("/Volumes/ritd-ag-project-rd00lq-jamfe87/GIS_Analysis/dataProcessed/GLA_Ind_Land_Baseline_2015/wide_DF.csv")
fullA1 <- merge(mpl, wide_DF, by = "LSOA11CD")

Map of Industrial Land highlighting LSOAs with Manufacturing Jobs Count and Percentage > 3rd QU (Industrial Land area by ‘TYPE_2015’)

Interactive Map
https://npalomin.github.io/pnum/fullA1_m.html

# fullA1_m.i <- fullA1_m + tm_facets(as.layers = TRUE) + tm_basemap(server="Esri.WorldImagery")
# tmap_mode("view")
# fullA1_m.i
# tmap_mode("plot")
#tmap_save(fullA1_m.i, "fullA1_m.html")