| title | author | date | output |
|---|---|---|---|
Part 1: Comparing the density of mobile phone cell towers with population: Analysis |
Alessandra Sozzi |
2017-08-08 |
github_document |
This notebook follows along the analysis produced for the publication Comparing the density of mobile phone cell towers with population part of the ONS Methodology Working Paper Series
The relationship between official estimates of UK residential and workday population1 density and the density of mobile phone cell-towers is investigated using freely available and open-sourced data on cell-tower locations from OpenCellID.
The next table illustrates a snapshot of the OpenCellID database updated at 16 January 2017.
Multiple GPS measurements referring to the same antenna are averaged to estimate its location more accurately. The main fields are: mobile technology (radio), mobile country code (mcc)2, mobile network (net), cell, longitude (lon), latitude (lat), range, samples (number of measurements), changeable (chg), created (timestamp of first measurement), updated (timestamp of last measurement). More detail on the definitions of these fields are in Appendix A of the publication.
head(ct)## cell radio mcc net lon lat range samples changeable
## 1 3564 GSM 234 55 -1.891770 49.68383 6912 48 1
## 2 3505 GSM 234 55 -1.904799 49.68576 7083 34 1
## 3 3628 GSM 234 55 -1.846508 49.63674 24 3 1
## 4 40081 GSM 234 50 -1.942688 49.70964 19160 59 1
## 5 40220 GSM 234 50 -1.992018 49.70110 15596 5 1
## 6 3097 GSM 234 50 -1.938819 49.69829 0 1 1
## created updated
## 1 1309376492 1309376504
## 2 1309376492 1309376504
## 3 1309376492 1309376492
## 4 1309376502 1458857843
## 5 1309376502 1458857843
## 6 1309376502 1309376502
The number of cell-towers associated with UK MCCs and listed in the OpenCellID database as per date 16 January 2017 is 1,427,795.
The latitude and longitude coordinates of 1,382,999 cell-towers were mapped2 into 2015 UK mainland LAD boundaries, the remaining 44,796 cell-towers being unallocated as they were based in Northern Ireland or their estimated position was in water around the UK coastline.
LAD = mutate(LAD,
NP = NUMPOINTS / 1000,
res = population_2015 / 1000,
x_1000_res = NUMPOINTS/(population_2015/1000.0))
head(LAD)## LAD15CD LAD15NM population_2015 NUMPOINTS wd_pop NP
## 1 E06000001 Hartlepool 92493 1343 88452 1.343
## 2 E06000002 Middlesbrough 139509 1903 146734 1.903
## 3 E06000003 Redcar and Cleveland 135275 1327 124661 1.327
## 4 E06000004 Stockton-on-Tees 194803 3027 190810 3.027
## 5 E06000005 Darlington 105389 1706 108660 1.706
## 6 E06000006 Halton 126528 2765 125593 2.765
## res x_1000_res
## 1 92.493 14.520018
## 2 139.509 13.640697
## 3 135.275 9.809647
## 4 194.803 15.538775
## 5 105.389 16.187648
## 6 126.528 21.852871
Using 2015 mid-year residential population estimates for the UK mainland at LAD level, the next Figure shows a scatter plot of the count of cell-towers against residents. It shows that there is a reasonable approximately linear relationship as depicted by a LOESS regression line. The correlation is 0.84.
# Scatter plot of cell tower counts vs. residential population (LADs across UK mainland)
ggplot(LAD, aes(NP, res)) +
geom_point(size = 2, alpha = 0.8, stroke= 0) +
geom_text(data=filter(LAD, res > 400 | NP > 10 | LAD15NM == "City of London"), aes(NP, res,label=LAD15NM), check_overlap = TRUE, hjust=-0.1, vjust=0, size = 4)+
stat_smooth(method = 'loess') +
xlab("LAD Cell Towers count in '000s") +
ylab("LAD UK mainland Residential Population in '000s") 
Within LADs in England and Wales only, correlation between cell-tower density and residential population is 0.83 and with workday population is 0.86.
LAD_EN = LAD %>% filter(grepl("E|W", LAD15CD))
head(LAD_EN)## LAD15CD LAD15NM population_2015 NUMPOINTS wd_pop NP
## 1 E06000001 Hartlepool 92493 1343 88452 1.343
## 2 E06000002 Middlesbrough 139509 1903 146734 1.903
## 3 E06000003 Redcar and Cleveland 135275 1327 124661 1.327
## 4 E06000004 Stockton-on-Tees 194803 3027 190810 3.027
## 5 E06000005 Darlington 105389 1706 108660 1.706
## 6 E06000006 Halton 126528 2765 125593 2.765
## res x_1000_res
## 1 92.493 14.520018
## 2 139.509 13.640697
## 3 135.275 9.809647
## 4 194.803 15.538775
## 5 105.389 16.187648
## 6 126.528 21.852871
# Corraletion between cell tower counts and Resiential population at Local Authority District Level (England and Wales only)
round(cor(LAD_EN$NUMPOINTS, LAD_EN$population_2015), 2) # 0.83## [1] 0.83
# Corraletion between cell tower counts and WorkDay population at Local Authority District Level (England and Wales only)
round(cor(LAD_EN$NUMPOINTS, LAD_EN$wd_pop), 2) # 0.86## [1] 0.86
The next Figure shows the distribution across all LADs of the number of cell-towers per 1000 residents. The mean is around 23 cell towers per 1000 residents but there is one outlier having 502 cell towers in the City of London LAD, an area with a much larger workday population compared with its residential population.
ggplot(LAD, aes(x_1000_res, y=..density..)) +
geom_histogram(stat = "bin", bins = 200, col="gray",
fill="#ef3b2c") +
ylab("Density") +
xlab("N° cell towers x1000 residents") 
# Summary Statistics
summary(LAD['x_1000_res'])## x_1000_res
## Min. : 0.00
## 1st Qu.: 15.56
## Median : 20.69
## Mean : 23.64
## 3rd Qu.: 26.24
## Max. :502.51
In London and other areas around major urban LADs, it might be expected that the residential population is very different to the workday population. Correlations were therefore run specifically for the 33 Greater London LADs. This revealed that the correlation between residential population and cell tower density in this region is very poor at 0.37 whilst by using workday population the correlation improves up to 0.91.
London_LAD = filter(LAD, grepl('E090000', LAD15CD))
head(London_LAD)## LAD15CD LAD15NM population_2015 NUMPOINTS wd_pop NP
## 1 E09000001 City of London 8760 4402 360075 4.402
## 2 E09000002 Barking and Dagenham 201979 3181 169117 3.181
## 3 E09000003 Barnet 379691 7636 314492 7.636
## 4 E09000004 Bexley 242142 3543 196519 3.543
## 5 E09000005 Brent 324012 6239 278874 6.239
## 6 E09000006 Bromley 324857 5513 269290 5.513
## res x_1000_res
## 1 8.760 502.51142
## 2 201.979 15.74916
## 3 379.691 20.11109
## 4 242.142 14.63191
## 5 324.012 19.25546
## 6 324.857 16.97054
# Corraletion between cell tower counts and Resiential population at Local Authority District Level (London only)
round(cor(London_LAD$NUMPOINTS, London_LAD$population_2015), 2) # 0.37## [1] 0.37
# Corraletion between cell tower counts and WorkDay population at Local Authority District Level (London only)
round(cor(London_LAD$NUMPOINTS, London_LAD$wd_pop), 2) # 0.91## [1] 0.91
For all cell-towers in England and Wales (1,262,507 in total), the latitude and longitude coordinates were mapped2 into MSOA boundaries.
# Number of cell towers per MSOA vs. number of residents per MSOA. England and Wales
MSOA_EW = mutate(MSOA_EW,
NP = NUMPOINTS / 1000,
res = mid_2015_res_pop / 1000,
wd = wd_pop / 1000)
head(MSOA_EW)## MSOA11NM MSOA11CD wd_pop NUMPOINTS mid_2015_res_pop NP res
## 1 Darlington 001 E02002559 15222 733 8900 0.733 8.900
## 2 Darlington 002 E02002560 3919 36 6276 0.036 6.276
## 3 Darlington 003 E02002561 3801 41 5506 0.041 5.506
## 4 Darlington 004 E02002562 4904 37 5947 0.037 5.947
## 5 Darlington 005 E02002563 4012 56 5393 0.056 5.393
## 6 Darlington 006 E02002564 6727 30 9572 0.030 9.572
## wd
## 1 15.222
## 2 3.919
## 3 3.801
## 4 4.904
## 5 4.012
## 6 6.727
The next Figure shows that there is no linear relationship between cell-tower counts and residential population across MSOAs in England and Wales: the correlation is 0.18.
# Number of cell towers per MSOA vs. number of residents per MSOA. England and Wales
ggplot(MSOA_EW, aes(NP, res)) +
geom_point(size = 2, alpha = 0.8, stroke= 0) +
stat_smooth(method = "loess") +
xlab("MSOA Cell Towers count in '000s") +
ylab("MSOA E&W Residential Population in '000s") +
geom_text(data=filter(MSOA_EW, res > 15 | (NP > 1.5 & NP < 4)),
aes(NP, res,label=MSOA11NM), check_overlap = TRUE, hjust=-0.06, vjust=0.2, size = 4)+
geom_text(data=filter(MSOA_EW, NP > 4),
aes(NP, res,label=MSOA11NM), check_overlap = TRUE,hjust=0.8, vjust=1.2, size = 4)+
geom_text(data=filter(MSOA_EW, res < 3),
aes(NP, res,label=MSOA11NM), check_overlap = TRUE,hjust=-0.08, vjust=0.2, size = 4)
# Corraletion between cell tower counts and Resiential population at MSOA Level (England & Wales)
round(cor(MSOA_EW$NUMPOINTS, MSOA_EW$mid_2015_res_pop),2) # 0.18## [1] 0.18
Although workday population is better correlated with cell-tower counts, this correlation is still weak at 0.51.
# Number of cell towers per MSOA vs. number of workday population per MSOA. England and Wales
ggplot(MSOA_EW, aes(NP, wd)) +
geom_point(size = 2, alpha = 0.8, stroke= 0) +
stat_smooth(method = "loess") +
xlab("MSOA Cell Towers count in '000s") +
ylab("MSOA E&W Workday Population in '000s") +
geom_text(data=filter(MSOA_EW, (wd > 80 & wd < 300) | (NP > 1.5 & NP < 4)),
aes(NP, wd,label=MSOA11NM), check_overlap = TRUE, hjust=-0.06, vjust=0.2, size = 4)+
geom_text(data=filter(MSOA_EW, NP > 4),
aes(NP, wd,label=MSOA11NM), check_overlap = TRUE,hjust=1.1, size = 4)
# Corraletion between cell tower counts and WorkDay population at Local Authority District Level (London only)
round(cor(MSOA_EW$NUMPOINTS, MSOA_EW$wd_pop), 2) # 0.51## [1] 0.51
The poor correlation between population density and cell tower density across the country is possibly due to the siting of large numbers of cell towers on major transport routes, typically in areas of low population density, as in Eden LAD identified above.
A high population density exists across the whole Greater London area and even though there are still main transport routes, these will similarly run through areas of high population density. Using the workday population for London MSOAs only, the next Figure shows that the correlation with cell towers is a respectable 0.86 compared against a correlation of only 0.15 with 2015 residential population.
# London MSOAs
MSOA_London = filter(MSOA_EW, MSOA11CD %in% MSOAs_L$V1)
head(MSOA_London)## MSOA11NM MSOA11CD wd_pop NUMPOINTS mid_2015_res_pop NP res wd
## 1 Camden 001 E02000166 7082 288 8554 0.288 8.554 7.082
## 2 Camden 002 E02000167 7453 341 8596 0.341 8.596 7.453
## 3 Camden 003 E02000168 9209 185 9000 0.185 9.000 9.209
## 4 Camden 004 E02000169 6140 203 8524 0.203 8.524 6.140
## 5 Camden 005 E02000170 5615 124 9127 0.124 9.127 5.615
## 6 Camden 006 E02000171 5900 92 8667 0.092 8.667 5.900
ggplot(MSOA_London, aes(NP, wd)) + geom_point(size = 2, alpha = 0.8, stroke= 0) + stat_smooth(method = "loess") +
xlab("MSOA London Cell Towers count in '000s") +
ylab("MSOA London Workday Population in '000s") +
geom_text(data=subset(MSOA_London, (wd > 100 & wd < 300) | (NP > 1 & NP < 4)),
aes(NP, wd,label=MSOA11NM),
check_overlap = TRUE, hjust=-0.06, vjust=0.2, size = 4) +
geom_text(data=subset(MSOA_London, NP > 4),
aes(NP, wd,label=MSOA11NM), check_overlap = TRUE,hjust=1.1, size = 4)
## [1] 0.15
## [1] 0.86
Since the distortion caused by transport routes may not be as pronounced in London, a more focussed analysis was conducted in this region to see if correlations between cell towers and population density might be good at even smaller geographies, such as LSOA.
LSOA_London = mutate(LSOA_London,
NP = NUMPOINTS / 1000,
res = mid_2015_res_pop / 1000,
wd = wd_pop / 1000)
head(LSOA_London)## LSOA11CD LSOA11NM NUMPOINTS wd_pop mid_2015_res_pop
## 1 E01000001 City of London 001A 272 13465 1639
## 2 E01000002 City of London 001B 210 30461 1412
## 3 E01000003 City of London 001C 35 1561 1698
## 4 E01000005 City of London 001E 259 19450 1503
## 5 E01000006 Barking and Dagenham 016A 30 1172 1868
## 6 E01000007 Barking and Dagenham 015A 73 2405 2142
## NP res wd
## 1 0.272 1.639 13.465
## 2 0.210 1.412 30.461
## 3 0.035 1.698 1.561
## 4 0.259 1.503 19.450
## 5 0.030 1.868 1.172
## 6 0.073 2.142 2.405
ggplot(LSOA_London, aes(NP, wd)) +
geom_point(size = 2, alpha = 0.8, stroke= 0) +
stat_smooth(method = "loess") +
xlab("LSOA London Cell Towers count in '000s") +
ylab("LSOA London Workday Population in '000s") +
geom_text(data=subset(LSOA_London, (wd > 30 & wd < 200) | (NP > 0.5 & NP < 2.5)),
aes(NP, wd, label=LSOA11NM),
check_overlap = TRUE, hjust=-0.06, vjust=0.2, size = 4) +
geom_text(data=subset(LSOA_London, NP > 2.5),
aes(NP, wd, label=LSOA11NM), check_overlap = TRUE, hjust=1.1, size = 4)
Correlation with cell tower density across London LSOAs remains high at 0.82. This compares with 0.14 for similar correlation with residential population, indicating that cell towers are more likely to be positioned to cater for the expected peak demand for mobile telephony which, in London, are more likely to arise from populations present during the workday.
# Corraletion between cell tower counts and Resiential population at LSOA Level (London only)
round(cor(LSOA_London$NUMPOINTS, LSOA_London$mid_2015_res_pop), 2) # 0.14## [1] 0.14
# Corraletion between cell tower counts and WorkDay population at LSOA Level (London only)
round(cor(LSOA_London$NUMPOINTS, LSOA_London$wd_pop), 2) # 0.82## [1] 0.82
1 Based on Census 2011
2 A mobile country code (MCC) is used in combination with a mobile network code (MNC) to uniquely identify an MNO. The MCCs associated with the UK are 234 and 235.
2 All the mapping of cell-towers to the geographical boundaries has been achieved using QGIS 2.18.2 Quantum GIS Geographic Information System. Open Source Geospatial Foundation Project.