Data-Informed Thinking + Doing
Geospatial Data Analysis
Analyzing Denver's small cell nodes (used for enabling 5G-powered IoT)—using ggmap for R, GeoPandas for Python, and JuliaGeo for Julia.
The wireless carrier industry and the City and County of Denver have been building small cell infrastructure to expand 4G and 5G coverage and capacity, and accommodate growing demand for mobile data consumption. This type of expansion is also the enabler that businesses and municipalities need, in order to turn on (Internet of Things) IoT-powered smart cities, agriculture, factories, etc. Let’s take a look at where these small cell nodes are (and will be) deployed in Denver.
Getting Started
If you are interested in reproducing this work, here are the versions of R, Python, and Julia used (as well as the respective packages for each). Additionally, my coding style here is verbose, in order to trace back where functions/methods and variables are originating from, and make this a learning experience for everyone—including me.
cat(R.version$version.string, R.version$nickname)R version 4.2.3 (2023-03-15) Shortstop Beaglerequire(devtools)
devtools::install_version("dplyr", version="1.1.2", repos="http://cran.us.r-project.org")
devtools::install_version("ggplot2", version="3.4.2", repos="http://cran.us.r-project.org")
devtools::install_version("ggmap", version = "3.0.2", repos = "http://cran.us.r-project.org")library(dplyr)
library(ggplot2)
library(ggmap)import sys
print(sys.version)3.11.4 (v3.11.4:d2340ef257, Jun 6 2023, 19:15:51) [Clang 13.0.0 (clang-1300.0.29.30)]!pip install pyreadr==0.4.7
!pip install numpy==1.25.1
!pip install pandas==2.0.3
!pip install plotnine==0.12.1
!pip install scipy==1.11.1import pyreadr
import numpy
import pandas
import plotnine
from scipy import statsusing InteractiveUtils
InteractiveUtils.versioninfo()Julia Version 1.9.2
Commit e4ee485e909 (2023-07-05 09:39 UTC)
Platform Info:
OS: macOS (x86_64-apple-darwin22.4.0)
CPU: 8 × Intel(R) Core(TM) i5-8259U CPU @ 2.30GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-14.0.6 (ORCJIT, skylake)
Threads: 1 on 8 virtual cores
Environment:
DYLD_FALLBACK_LIBRARY_PATH = /Library/Frameworks/R.framework/Resources/lib:/Library/Java/JavaVirtualMachines/jdk1.8.0_241.jdk/Contents/Home/jre/lib/serverusing Pkg
Pkg.add(name="RData", version="1.0.0")
Pkg.add(name="CSV", version="0.10.11")
Pkg.add(name="DataFrames", version="1.5.0")
Pkg.add(name="CategoricalArrays", version="0.10.8")
Pkg.add(name="Colors", version="0.12.10")
Pkg.add(name="Cairo", version="1.0.5")
Pkg.add(name="Gadfly", version="1.3.4")
Pkg.add(name="StatsBase", version="0.33.21")using RData
using CSV
using DataFrames
using CategoricalArrays
using Colors
using Cairo
using Gadfly
using StatsBaseImporting and Examining Dataset
str(small_cells)'data.frame': 2517 obs. of 22 variables:
$ owner_name : Factor w/ 6 levels "AT&T","Crown Castle (T-Mobile)",..: 1 5 2 2 2 2 5 2 1 2 ...
$ applicant_company_name: Factor w/ 6 levels "BV","Crown Castle",..: 1 5 2 2 2 2 5 2 1 2 ...
$ permit_master_number : Factor w/ 43 levels "2017-ENCROACHMENT-0000080",..: NA 2 42 42 42 42 40 42 15 11 ...
$ permit_ref_number : Factor w/ 111 levels "2016-ENCROACHMENT-0000001",..: NA 46 110 110 110 110 107 110 NA 74 ...
$ node_group : Factor w/ 332 levels " 2018 Group 1B",..: 151 65 267 265 267 166 146 265 88 98 ...
$ node_id : chr "COL07002F_R01" "HIGHLANDS SC 30" "DN0315" "DN0791" ...
$ node_address : chr "1405 Little Raven St." "2595 DECATUR ST DENVER CO 80211" "2719 W 21st Ave" "1525 Valentia St" ...
$ node_n : num 39.8 39.8 39.7 39.7 39.8 ...
$ node_e : num -105 -105 -105 -105 -105 ...
$ node_type : Factor w/ 8 levels "Type 1A, Utility Pole Mounted",..: 4 7 2 2 2 2 4 2 7 7 ...
$ status : Ord.factor w/ 3 levels "Proposed"<"Future Planned 1-2 Years"<..: 3 3 NA NA NA NA 1 NA 2 3 ...
$ pole_material : Factor w/ 4 levels "Metal","Metal ",..: 1 1 4 4 4 3 1 4 1 1 ...
$ pole_manufacturer : Factor w/ 6 levels "Comptek","CompTek",..: 2 2 3 3 3 3 2 3 4 5 ...
$ pole_model : Factor w/ 8 levels "1002661","Class 4 Wood Pole",..: 7 4 4 4 4 4 4 4 7 4 ...
$ pole_height_ft : int 35 31 0 0 0 27 34 0 35 25 ...
$ pole_color : Factor w/ 10 levels "Agate Grey (RAL 7038)",..: 5 5 8 8 8 8 5 8 5 8 ...
$ rf_equipment : Factor w/ 31 levels "","1","2","3",..: 3 14 13 13 13 13 14 13 3 13 ...
$ intended_use : Factor w/ 3 levels "Multi Carrier",..: 3 3 3 3 3 3 3 3 3 1 ...
$ asset_owner : Factor w/ 11 levels " ","ATT","BV on behalf of ATT",..: 3 8 9 9 9 9 9 9 3 5 ...
$ site_id : Factor w/ 1144 levels "COL06929F_R01 - LTE1C - Micro Cell - STAPL - 2017H2TUsc (CRAN_RUTH_STAPL_064)",..: 99 1143 707 907 709 662 1143 829 238 731 ...
$ fixed_asset_id : chr "14809978" "387368" "DN0315" "DN0791" ...
$ council_district : Factor w/ 11 levels "1","2","3","4",..: 9 1 1 8 1 7 7 9 9 10 ...# Identify missing values (NA)
missing_values <- is.na(small_cells)
# Count NA
missing_counts <- colSums(is.na(small_cells))
missing_counts owner_name applicant_company_name permit_master_number permit_ref_number node_group node_id node_address node_n node_e node_type status pole_material pole_manufacturer pole_model pole_height_ft pole_color rf_equipment intended_use asset_owner site_id fixed_asset_id council_district
0 0 263 635 3 0 0 0 0 0 419 0 0 0 0 0 0 0 0 0 0 0 # Locate NA
row_indices <- which(missing_values, arr.ind = TRUE)[, "row"]
col_indices <- which(missing_values, arr.ind = TRUE)[, "col"]
missing_indices <- cbind(row_indices, col_indices)
missing_indices row_indices col_indices
[1,] 1 3
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[1320,] 2517 11Visualizing Categorical Data
# small_cells %>%
# ggplot2::ggplot(aes(x=owner_name, y=(..count..), fill=status)) +
# ggplot2::geom_bar(position="stack", stat="count") +
# ggplot2::scale_x_discrete(limits=c("Verizon", "AT&T", "Crown Castle (T-Mobile)", "Zayo", "Mobilitie (Sprint)", "SQF")) +
# ggplot2::scale_y_continuous(expand=c(0, 0), position="right") + # Scale
# # ggplot2::scale_fill_manual(values=c("Existing"=palette_michaelmallari_r[2], "Proposed"=palette_michaelmallari_r[19], "Future Planned 1-2 Years"=palette_michaelmallari_r[20])) +
# ggplot2::labs(
# title="Denver's 5G-Readiness and Commitments, Especially From Verizon",
# alt="Denver's 5G-Readiness and Commitments, Especially From Verizon",
# subtitle="Count of small cell sites, grouped by owner and status, 2021",
# x=NULL,
# y=NULL,
# fill=NULL,
# caption="Source: https://www.denvergov.org/opendata/dataset/city-and-county-of-denver-small-cell-node"
# ) +
# theme_michaelmallari_r()Visualizing Geospatial Data
# Center map on Colfax & Broadway
denver <- c(lon = -104.98741062659272, lat = 39.7400672449674)
denver_map <- ggmap::get_map(denver, zoom = 12, source = "stamen", maptype = "toner")# ggmap::ggmap(denver_map, base_layer = ggplot(small_cells, aes(node_e, node_n))) +
# ggplot2::geom_point(aes(color=council_district), size=0.33) +
# # ggplot2::scale_y_continuous(expand=c(0, 0), position="right") + # Scale
# ggplot2::labs(
# title="Small Cell Nodes Located by Council District in Denver",
# alt="Small Cell Nodes Located by Council District in Denver",
# subtitle=NULL,
# x="Latitude",
# y="Longitude",
# color="Council District",
# caption="Source: https://www.denvergov.org/opendata/dataset/city-and-county-of-denver-small-cell-node"
# )small_cells_att <- small_cells %>%
filter(owner_name == "AT&T")
# ggmap::ggmap(denver_map, base_layer = ggplot(small_cells_att, aes(node_e, node_n))) +
# ggplot2::geom_point(aes(color = status), size = 0.5, alpha = 0.75) +
# ggplot2::xlab("Latitude") +
# ggplot2::ylab("Longitude") +
# ggplot2::labs(title = "AT&T Small Cell Nodes in Denver", color = "Status") +
# ggplot2::scale_color_manual(values=c("#00a6c8", "#eb3300", "#9063cd")) +
# ggplot2::theme(
# plot.title = element_text(size = 14, face = "bold", hjust = 0.5)
# )# small_cells_tmobile_sprint <- small_cells %>%
# filter(owner_name == "Crown Castle (T-Mobile)" | owner_name == "Mobilitie (Sprint)")
#
# ggmap::ggmap(denver_map, base_layer = ggplot(small_cells_tmobile_sprint, aes(node_e, node_n))) +
# ggplot2::geom_point(aes(colour = status), size = 0.5, alpha = 0.75) +
# ggplot2::xlab("Latitude") +
# ggplot2::ylab("Longitude") +
# ggplot2::labs(title = "T-Mobile/Sprint Small Cell Nodes in Denver", colour = "Status") +
# ggplot2::scale_color_manual(values=c("#00a6c8", "#eb3300")) +
# ggplot2::theme(
# plot.title = element_text(size = 14, face = "bold", hjust = 0.5)
# )# small_cells_verizon <- small_cells %>%
# filter(owner_name == "Verizon")
#
# ggmap::ggmap(denver_map, base_layer = ggplot(small_cells_verizon, aes(node_e, node_n))) +
# ggplot2::geom_point(aes(colour = status), size = 0.5, alpha = 0.75) +
# ggplot2::xlab("Latitude") +
# ggplot2::ylab("Longitude") +
# ggplot2::labs(title = "Verizon Small Cell Nodes in Denver", colour = "Status") +
# ggplot2::scale_color_manual(values=c("#00a6c8", "#eb3300")) +
# ggplot2::theme(
# plot.title = element_text(size = 14, face = "bold", hjust = 0.5)
# )References
- Bivand, R. S., Pebesma, E., & Gómez-Rubio, V. (2013). Applied Spatial Data Analysis with R (2nd ed.). Springer. https://doi.org/10.1007/978-1-4614-7618-4
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