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.

Once this has been figure out, proceed with the following companies and tickers:

  • 7203 JP TOYOTA MOTOR
  • 1211 HK BYD COMPANY
  • GM US GENERAL MOTORS C
  • 005380 KS HYUNDAI MOTOR
  • MBG GR MERCEDES-BENZ GR
  • BMW GR BMW AG
  • VOW GR VOLKSWAGEN AG
  • RACE US FERRARI NV
  • 7267 JP HONDA MOTOR CO
  • MSIL IN MARUTI SUZUKI IN
  • MM IN MAHINDRA & MAHIN
  • P911 GY DR ING HC F PORS
  • 000270 KS KIA CORP
  • BJAUT IN BAJAJ AUTO LTD
  • STLAM IM STELLANTIS NV
  • SERES GROUP CO-A SERES GROUP CO-A
  • 7269 JP SUZUKI MOTOR
  • 600104 CH SAIC MOTOR-A
  • 2333 HK GWMOTOR
  • GELYF US GEELY AUTOMOBILE
  • EIM IN EICHER MOTORS
  • HYUNDAI IN HYUNDAI MOTOR IN
  • TVSL IN TVS MOTOR CO LTD
  • RIVN US RIVIAN AUTOMOT-A
  • LI US Equity LI AUTO INC -ADR
  • 9868 HK Equity XPENG INC-A SHRS
  • 7270 JP Equity SUBARU CORP
  • 000625 CH Equity CHONGQING CHAN-B
  • TMPV IN Equity TATA MOTORS PASS
  • P911 GY Equity PORSCHE AUTO-PRF
  • HMCL IN Equity HERO MOTOCORP LT
  • NIO US Equity NIO INC - ADR
  • RNO FP Equity RENAULT SA
  • VOLCARB SS Equity VOLVO CAR AB-B
  • 601238 CH Equity GUANGZHOU AUTO-H
  • 489 HK Equity DONGFENG MOTOR-H
  • FROTO TI Equity FORD OTO
  • 7201 JP Equity NISSAN MOTOR CO
  • 7272 JP Equity YAMAHA MOTOR CO
  • 9863 HK Equity ZHEJIANG LEAPM-H
  • 601777 CH Equity CHONGQING AFAR-A
  • 600733 CH Equity BAIC BLUEPARK-A
  • 603129 CH Equity ZHEJIANG CFMOT-A
  • 7261 JP Equity MAZDA MOTOR
  • 603766 CH Equity LONCIN MOTOR C-A
  • 1585 HK Equity YADEA GROUP HOLD
  • TOASO TI Equity TOFAS
  • 7211 JP Equity MITSUBISHI MOTOR
  • LCID US Equity LUCID GROUP INC
  • 301345 CH Equity ZHEJIANG TAOTA-A
  • FML IN Equity FORCE MOTORS LTD
  • 1114 HK Equity BRILLIANCE CHINA
  • GTCAP PM Equity GT CAPITAL HOLD
  • 600418 CH Equity ANHUI AUTOMOBI-A
  • HOG US Equity HARLEY-DAVIDSON
  • 600006 CH Equity DONGFENG AUTO-A
  • 1958 HK Equity BAIC MOTOR-H
  • MHSC IN Equity MAHARASHTRA SC
  • 2206 TT Equity SANYANG MOTOR
  • PSNY US Equity POLESTAR A-A ADS
  • HLI MK Equity HONG LEONG INDS
  • OTKAR TI Equity OTOKAR
  • 2201 TT Equity YULON MOTOR
  • 2204 TT Equity CHINA MOTOR
  • 7222 JP Equity NISSAN SHATAI
  • AML LN Equity ASTON MARTIN
  • LOT US Equity LOTUS TECHNOLOGY
  • ATLH PA Equity ATLAS HONDA
  • PIA IM Equity PIAGGIO & C. SPA
  • GBCO EY Equity GB CORP
  • JYGCLZ CH Equity JIANGSU YUEDA -A
  • BJAUT IN Equity BAJAJ MOBILITY A
  • 003620 KS Equity KG MOBILITY CO
  • DRB MK Equity DRB-HICOM BHD
  • 2227 TT Equity YULON NISSAN
  • LVWR US Equity LIVEWIRE GROUP I
  • 8937 TT Equity HER CHEE
  • 1656311D CH Equity NIU TECHNOLO-ADR
  • 3333 HK Equity CHINA EVERGRANDE
  • IMS IM Equity IMMSI SPA
  • HCAR PA Equity HONDA ATLAS CAR
  • 0K4R LN Equity CARS MOTORCYCLES
  • NRDE US Equity NU RIDE INC
Code 
# 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
Code 
# 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
Code 
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.

Code 
library(tidyverse)
library(rio)
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)
Code 
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.