Introduction
This report analyses the ‘Land Use’ and ‘Industrial Type’ of the units (building polygons, n = 2128) selected for the SIA survey after data pre-processing (geometric generalisation)
df <- geojsonio::geojson_read("BUILDINGS_ed.geojson", what = "sp")
## Registered S3 method overwritten by 'geojsonio':
## method from
## print.location dplyr
ST1 <- dff %>%
group_by(TYPE_2015, landUSE) %>%
summarise(Frequency = n(), "Area(sqm)" = round(sum(b_area),0)) %>%
arrange(desc(Frequency), .by_group = TRUE)
colnames(ST1) <- c("Ind. Designation", "Land Use", "Count", "Area(sqm)")
htmlTable(ST1, caption="SIA building polygons summary", col.columns = c("none", "#F7F7F7"), css.cell="padding-left:1em; padding-right:1em;", align = "lrrr", rnames=F)
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SIA building polygons summary
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|
Ind. Designation
|
Land Use
|
Count
|
Area(sqm)
|
|
LSIS
|
Warehouses
|
97
|
68047
|
|
LSIS
|
General Industry
|
27
|
15721
|
|
LSIS
|
Retail
|
14
|
2544
|
|
LSIS
|
Open Storage
|
4
|
1767
|
|
LSIS
|
Utilities
|
3
|
200
|
|
LSIS
|
Light Industry
|
1
|
215
|
|
NAL
|
General Industry
|
754
|
179606
|
|
NAL
|
Utilities
|
320
|
24172
|
|
NAL
|
Warehouses
|
248
|
64362
|
|
NAL
|
Waste management and recycling
|
19
|
5309
|
|
NAL
|
Light Industry
|
10
|
28560
|
|
NAL
|
Open Storage
|
7
|
1262
|
|
NAL
|
Other
|
7
|
361
|
|
NAL
|
Vacant Industrial Land
|
1
|
250
|
|
SIL
|
General Industry
|
214
|
61890
|
|
SIL
|
Warehouses
|
197
|
88571
|
|
SIL
|
Waste management and recycling
|
71
|
52894
|
|
SIL
|
Light Industry
|
68
|
10515
|
|
SIL
|
Land for rail
|
27
|
5609
|
|
SIL
|
Vacant Industrial Land
|
10
|
1746
|
|
SIL
|
Retail
|
9
|
2169
|
|
SIL
|
Mixed-ise (Non-industrial only)
|
8
|
366
|
|
SIL
|
Utilities
|
6
|
1671
|
|
SIL
|
Self Storage
|
5
|
1906
|
|
SIL
|
Recreation and leisure
|
1
|
204
|
breaks_sdm <- c(0,15,20,50,100,500,1000,5000,10000,50000,200000)
NAL_S <- dff %>%
filter(TYPE_2015 == "NAL") %>%
select(b_area, LU_Co_2015, landUSE)
nalsDF <- as.data.frame(table(cut(NAL_S$b_area, breaks_sdm)))
htmlTable(nalsDF, caption="NALs count by sqm range", col.columns = c("none", "#F7F7F7"), css.cell="padding-left:1em; padding-right:1em;", align = "lrrr", rnames=F, tfoot="0,15,20,50,100,500,1000,5000,10000,50000,200000")
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NALs count by sqm range
|
|
Var1
|
Freq
|
|
(0,15]
|
0
|
|
(15,20]
|
194
|
|
(20,50]
|
375
|
|
(50,100]
|
250
|
|
(100,500]
|
418
|
|
(500,1e+03]
|
84
|
|
(1e+03,5e+03]
|
40
|
|
(5e+03,1e+04]
|
2
|
|
(1e+04,5e+04]
|
3
|
|
(5e+04,2e+05]
|
0
|
|
0,15,20,50,100,500,1000,5000,10000,50000,200000
|
breaks_sdm1 <- c(0,15,20,50,100,500,1000)
uti <- NAL_S %>%
filter(landUSE == "Utilities")
utDF <- as.data.frame(table(cut(uti$b_area, breaks_sdm1)))
htmlTable(utDF, caption="Utilities count by sqm range", col.columns = c("none", "#F7F7F7"), css.cell="padding-left:1em; padding-right:1em;", align = "lrrr", rnames=F, tfoot="0,15,20,50,100,500,1000")
|
Utilities count by sqm range
|
|
Var1
|
Freq
|
|
(0,15]
|
0
|
|
(15,20]
|
115
|
|
(20,50]
|
124
|
|
(50,100]
|
31
|
|
(100,500]
|
40
|
|
(500,1e+03]
|
7
|
|
0,15,20,50,100,500,1000
|