Mapping global venture capital investment

September 14, 2016
By

(This article was first published on r-bloggers – SHARP SIGHT LABS, and kindly contributed to R-bloggers)

vc-investment_worldwide-map_1138x525

Claims of “the end of geography” and the flatness of the world notwithstanding, place still matters today.

Discussing why place matters is somewhat beyond the scope of this post, so I will direct you to the excellent work of Parag Khanna and his book Connectography. To put it simply, the the future of business and international relations will be best described by networks: nodes (cities, small-groups, individuals, power-centers), connectivity (between places and within places), centrality (geographically, culturally, economically), and distance will be critically important.

Having said that, I’ll repeat: place still matters. Location matters. And cities (in particular) matter.

Innovation is concentrated in a few cities

This is strongly true of innovation. A small number of cities account for the vast majority of innovation (40 city regions generate roughly 90% of all world innovation). Moreover, innovation is not evenly distributed; some cities are much more innovative than others.

With this in mind, I created the above map of venture capital spending, by city. Although VC spending isn’t the same thing as innovation, it’s a fairly decent proxy, and at least gives us a sense of where innovation is happening.

The data is from 2012, and comes from the Martin Prosperity Institute, by way of Citylab. Keep in mind that here, I’ve only used the top 20 cities (the original MPI data includes many more cities).

The size of each circle is scaled for the amount of VC spending.

The Bay Area dominates VC investment

At a glance, you can see from the map that the US dominates VC spending, with the West Coast and East Coast showing particularly high levels of investment. However, it’s somewhat difficult to accurately judge the relative magnitudes in a map format.

A better tool for judging the magnitudes (and relative differences) is the bar chart.
(The human visual system judges length more accurately than area.)

Having said that, here is a bar chart of the same data.

Quickly, you’ll notice that San Francisco and San Jose (AKA: Silicon Valley) have quite a bit more VC investment than almost anywhere else. In fact, combined, they account for roughly 25% of VC investment.

vc-investment_bar-chart_680x692

Code

If you want to reproduce the charts (and play around) here is the R code:

# LOAD LIBRARIES
library(ggalt)
library(ggplot2)
library(maps)
library(dplyr)
library(readr)


# GET DATA
df.vc_totals <- read_csv("http://sharpsightlabs.com/wp-content/uploads/2016/09/vc_investment_totals.csv")



# INSPECT
head(df.vc_totals)
str(df.vc_totals)


# GET WORLD MAP
map.world <- map_data ("world")



# SIMPLE MAP WITH POINTS
# - this is just to test the data 
ggplot() +
  geom_polygon(data = map.world, aes(x = long, y = lat, group = group)) +
  geom_point(data = df.vc_totals, aes(x = longitude, y = latitude), color = "red")



#-----------------------------------------
# CREATE FINAL MAP
# notes:
# 1. size is the total VC investment
# 2. there are two layers of points.  This is to have both the
#    point outline as well as an interior, but with different 
#    transparency levels (i.e., different alpha)
# 3. most of the formatting (i.e., theming) is just removing things
#    to make this simpler
#-----------------------------------------

ggplot() +
  geom_polygon(data = map.world, aes(x = long, y = lat, group = group),fill = "#002035",colour = "#114151", size = .25) +
  geom_point(data = df.vc_totals, aes(x = longitude, y = latitude, size = vc_investment_millions), color = "red", alpha = .15) +
  geom_point(data = df.vc_totals, aes(x = longitude, y = latitude, size = vc_investment_millions), color = "red", alpha = .8, shape = 1) +
  coord_proj("+proj=robin +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs") + # use robinson projection
  scale_size_continuous(range = c(1,20), breaks = c(500,2000,6000), name="Venture Capital Investment\n(USD, Millions)\n") +
  theme(text = element_text(family = "Gill Sans")) +
  theme(panel.background = element_rect(fill = "#000727")) +
  theme(panel.grid = element_blank()) +
  theme(axis.text = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(axis.title = element_blank()) +
  theme(legend.position = c(.17,.3)) +
  theme(legend.background = element_blank()) +
  theme(legend.key = element_blank()) +
  theme(legend.title = element_text(color = "#DDDDDD", size = 16)) +
  theme(legend.text = element_text(color = "#DDDDDD", size = 16)) 



#------------------------------------------
# BAR CHART
# - descending order from most VC to least
#------------------------------------------

df.vc_totals %>%
  ggplot(aes( x = reorder(metro, vc_investment_millions),y = vc_investment_millions)) +
  geom_bar(stat = "identity", fill = "#002035") +
  geom_text(aes(label = vc_investment_millions), hjust = 1.1, color = "#FFFFFF") +
  labs(y = "Millions of Dollars", title = "Venture Capital Investment by City") +
  coord_flip() +
  theme(text = element_text(family = "Gill Sans")) +
  theme(plot.title = element_text(size = 28, color = "#555555")) +
  theme(axis.title.y = element_blank()) +
  theme(panel.background = element_rect(fill = "#CCCCCC")) +
  theme(panel.grid.major = element_blank()) +
  theme(panel.grid.minor = element_blank())

The post Mapping global venture capital investment appeared first on SHARP SIGHT LABS.

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