The document analyzes electricity consumption data from New York City in 2010. It finds that commercial buildings consume the most electricity at 55% of the city's total. Large residential buildings consume 7% while small residential buildings consume 26%. The areas with the highest electricity consumption in large residential buildings are zip codes containing public housing. Four zip codes in midtown Manhattan between 42nd and 57th streets consume 20% of the total commercial electricity.

1. NEW YORK CITY’S
ELECTRICITY CONSUMPTION 2010
New Yorkers consume more energy inside than
Americans on average. In New York around 75%
of the greenhouse gas emissions come from
activities such as lighting, heating, cooling and
appliances, while on a national level that figure is
39%.
Source: http://www.nycedc.com/blog-entry/energy-consumption-new-york-city

2. We take a closer look on New York City’s
electricity consumption to figure out what is
actually happening within NYC buildings.

3. ELECTRICITY CONSUMPTION IN NEW YORK CITY 2010
Data source: NYC Open Data (www.nyc.gov). Data visualization by Marija Drobnjak and Lise White, Dec. 2013
Commercial
buildings use 55%
Institutional
buildings consume
of the city’s entire
electricity
consumption
12%
Large
residential
Small
residential
buildings consume
7%
buildings consume
26%
high
low

4. LARGE RESIDENTIAL BUILDINGS ELECTRICITY CONSUMPTION
Data source: NYC Open Data (www.nyc.gov). Data visualization by Marija Drobnjak and Lise White, Dec. 2013
Large residential buildings use 7 % of
New York City’s entire electricity consumption
Max consumption per year =
kWh per zip code area
100 million
The areas with highest large
residential consumption are
predominately zip codes
containing public housing
high
low

5. INSTITUTIONAL BUILDINGS ELECTRICITY CONSUMPTION
Data source: NYC Open Data (www.nyc.gov). Data visualization by Marija Drobnjak and Lise White, Dec. 2013
Institutional buildings use 12 % of New
York City’s entire electricity consumption
Max consumption per year = 467 million
kWh per zip code area
South Ozone park has the
highest consumption within
institutional buildings, here
lies JFK Airport
high
low

6. SMALL RESIDENTIAL BUILDINGS ELECTRICITY CONSUMPTION
Data source: NYC Open Data (www.nyc.gov). Data visualization by Marija Drobnjak and Lise White, Dec. 2013
Small residential buildings use 26 % of
New York City’s entire electricity consumption
Max consumption per year = 224 million
kWh per zip code area
Staten Island and The Upper
East and Upper West Side
have the highest small
residential energy
consumption
Staten Island has 2.7 people
per household where
Manhattan has 1.7
high
low

7. COMMERCIAL BUILDINGS ELECTRICITY CONSUMPTION
Data source: NYC Open Data (www.nyc.gov). Data visualization by Marija Drobnjak and Lise White, Dec. 2013
Commercial buildings use 55 % of New
York City’s entire electricity consumption
Max consumption per year = 1.4 billion
kWh per zip code area
4 zip codes between 42nd
and 57th street use 20 %
of the total commercial
consumption. They range
between 1.2 and 1.4
billion kWh use
high
low

8. The City of New York has recently released a
report benchmarking commercial building
consumption in NYC. This is the first time a city in
the nation releases private sector building data.
The objective is to make the general public aware
of the current consumption patterns and thereby
stimulate an interest in changing these.
Our data analysis fully supports the city’s initiative.
We therefore hope our data visualization can
function as a supporting tool to encourage change.
Source: http://www.nyc.gov/html/gbee/html/plan/ll84_scores.shtml

9. require(ggplot2)
# shape file, download from: http://www.census.gov/cgi-bin/geo/shapefiles2010/main
shape_fp <- " xx "
shape_fn <- "tl_2010_36_zcta510.shp"
electricity_fp <- " xx "
electricity_fn <- "Electric_Consumption.csv"
library(ggplot2)
library(maptools)
library(rgdal)
library(rgeos)
setwd(shape_fp)
ogrListLayers(shape_fn)
nyc_shp <- readOGR(shape_fn, layer="tl_2010_36_zcta510")
nycs <- fortify(nyc_shp, region= "ZCTA5CE10")
setwd(electricity_fp)
electric <- read.csv(electricity_fn, header=TRUE)
electric$loc1
electric$loc2
electric$loc3
electric$loc4
<<<<-
regexpr("n", as.character(electric$Zip.Code))
regexpr(",", as.character(electric$Zip.Code))
regexpr("-", as.character(electric$Zip.Code))
regexpr(")", as.character(electric$Zip.Code))
electric$Zip <- substr(as.character(electric$Zip.Code), 1, 5)
electric$Lat <- as.numeric(substr(as.character(electric$Zip.Code), electric$loc1 + 2, electric$loc2 - 1))
electric$Long <- as.numeric(substr(as.character(electric$Zip.Code), electric$loc3, electric$loc4 - 1))
electric$loc1
electric$loc2
electric$loc3
electric$loc4
<<<<-
NULL
NULL
NULL
NULL
electric$Building.type..service.class. <- gsub('Small residential', 'Small Residential', electric$Building.type..service.class.)
electric$Building.type..service.class. <- as.factor(electric$Building.type..service.class.)
electric$kwdecN <- as.numeric(cut(electric$Consumption..kWh.,quantile(electric$Consumption..kWh.,(0:10)/
10),include.lowest=TRUE,lable=TRUE))
summary(electric)
attach(electric)
elec.summ <- aggregate(electric, by=list(Zip, Building.type..service.class.), FUN = mean, na.rm=TRUE)
elec.summ.largeres <- elec.summ[elec.summ$Group.2=="Large Residential",]
elec.summ.largeres$id <- elec.summ.largeres$Group.1
elec.summ.largeres$Group.1 <- NULL
head(elec.summ.largeres)
summary(elec.summ.largeres)
head(nycs)
head(elec.summ.largeres)
formap <- merge(nycs, elec.summ.largeres, by="id", all.x = TRUE)
head(formap)
summary(formap)
nrow(nycs)
nrow(formap)
formap <- formap[order(formap$order), ]
rm(nycs)
ggplot(formap, aes(x = long, y = lat, group=group)) +
geom_polygon(aes(fill= kwdecN), color="grey") +
coord_cartesian(xlim=c(-74.3, -73.7), ylim=c(40.41, 40.92)) +
theme(plot.background = element_rect(fill='black', colour='black')) +
theme(panel.background = element_rect(fill="black")) +
theme(line = element_blank()) +
theme(text = element_blank()) +
theme(title = element_blank()) +
labs(x=NULL, y=NULL) +
scale_x_continuous(expand=c(0,0)) +
scale_fill_gradientn(colours = c("yellow" , "red"))
table(cut(electric$Consumption..kWh.,quantile(electric$Consumption..kWh.,(0:10)/10),include.lowest=TRUE,lable=TRUE))
getwd()
ggsave("elec.svg", width = 7, height = 7)
ggsave("elec.png", width= 7, height=7)