Automotives Example

In this example, I apply the matrix to the automotive industry

Set up Coding Environment

Find and Geocode Factories

Naively, we know from Bloomberg a list of publicly traded automotive companies. Use the geocode_factories.py script to search the internet for factory information and geocode them using openstreet maps.

# Set location to save geocodes
import os
import sys

# Add scripts directory to path so we can import geocode_factories
sys.path.insert(0, "E:/matrix/thematrix/scripts")
from geocode_factories import search_factories, geocode_from_csv

# Set directory for firm geocodes
output_dir = "E:/matrix/data/firms/geocodes"
os.makedirs(output_dir, exist_ok=True)

# Define output files
ford_output_1 = os.path.join(output_dir, "F_factory_geocodes.csv")
ford_output_2 = os.path.join(output_dir, "F_factory_info.csv")
tesla_output_1 = os.path.join(output_dir, "TSLA_factory_geocodes.csv")
tesla_output_2 = os.path.join(output_dir, "TSLA_factory_info.csv")

# Search Ford factories 
if not os.path.exists(ford_output_2):
    print("Geocoding Ford factories (Step 1: Search)...")
    search_factories("F US", "Ford Motor", ford_output_1)
    print("Geocoding Ford factories (Step 2: Detailed geocoding)...")
    geocode_from_csv("F US", ford_output_1, ford_output_2)
else:   
    print(f"Ford geocodes already exist at {ford_output_2}")

Ford geocodes already exist at E:/matrix/data/firms/geocodes\F_factory_info.csv

# Search Tesla factories 
if not os.path.exists(tesla_output_2):
    print("\nGeocoding Tesla factories (Step 1: Search)...")
    search_factories("TSLA US", "Tesla", tesla_output_1)
    print("Geocoding Tesla factories (Step 2: Detailed geocoding)...")
    geocode_from_csv("TSLA US", tesla_output_1, tesla_output_2)
else:
    print(f"Tesla geocodes already exist at {tesla_output_2}")

Tesla geocodes already exist at E:/matrix/data/firms/geocodes\TSLA_factory_info.csv

print("\nDone!")

Done!

Create a Map of Industry

Use the geocodes to create an interactive map of the factories and production locations for the industry in question.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.2     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(rio)

Attaching package: 'rio'

The following object is masked from 'package:reticulate':

    import

setwd("E:/matrix/")

ford_factories <- import("./data/firms/F_factory_info.csv")
tesla_factories <- import("./data/firms/geocodes/TSLA_factory_geocodes.csv")

# factories <- rbind(ford_factories, tesla_factories)

library(leaflet)

company_icons <- iconList(
    "F US" = makeIcon(
        iconUrl = "https://upload.wikimedia.org/wikipedia/commons/3/3e/Ford_logo_flat.svg",
        iconWidth = 30,
        iconHeight = 30
    )
)

leaflet(ford_factories) %>%
    addTiles() %>%
    addMarkers(
        ~longitude,
        ~latitude,
        icon = ~ company_icons[ticker],
        label = ~ paste0(facility_name, " (", ticker, ")"),
        popup = ~ paste(
            "<b>", facility_name, "</b><br>",
            "Ticker: ", ticker, "<br>",
            "Type: ", facility_type, "<br>",
            "Products: ", products, "<br>",
            "Active: ", status
        )
    )

Global distribution of manufacturing facilities

TODO: Add a version with bi-variate map of emissions and ecosystem services as a layer.