GreenBuildings1

GreenBuildings 1: Exploratory Analysis & Outlier Removal


1. Introduction

2. Exploratory Data Analysis

3. Connecting To BigQuery

4. Removing Outliers

5. Recomendations & Next Steps

Introduction


I started this project a while back with a goal of taking the 2016 NYC Benchmarking Law building energy usage data and do something interesting with it. I originally attmpted to clean and analyze this data set to try to find ways to reduce builings' energy usage and subsequently their green house gas emissions. After a few iterations I thought it might be interesting to see if I could predict the emission of green house gases from buildings by looking at their age, energy and water consumption as well as other energy consumption metrics. This is somewhat of a difficult task as the data was very messy and in this first blogpost I will cover how to perform,

  • Exploratory data analysis
  • Identify and remove outliers

Since I will completing this project over multiple days and using Google Cloud, I will go over the basics of using BigQuery for storing the datasets so I won't have to start all over again each time I work on it. At the end of this blogpost I will summarize the findings, and give some specific recommendations to reduce mulitfamily and office building energy usage. The source code for this project can be found here.


Data


The NYC Benchmarking Law requires owners of large buildings to annually measure their energy and water consumption in a process called benchmarking. The law standardizes this process by requiring building owners to enter their annual energy and water use in the U.S. Environmental Protection Agency's (EPA) online tool, ENERGY STAR Portfolio Manager® and use the tool to submit data to the City. This data gives building owners information about a building's energy and water consumption compared to similar buildings, and tracks progress year over year to help in energy efficiency planning.

Benchmarking data is also disclosed publicly and can be found here. I analyzed the 2016 data and my summary of the findings and recommendations for reducing energy consumption in New York City buildings are discussed in the conclusions post.

The data comes from the year 2016 and is quite messy and a lot of cleaning is necessary to do analysis on it. The cleaning process was made more difficult because the data was stored as strings with multiple non-numeric values which made converting the data to its proper type a more involved process. One thing to keep in mind through out this post is that this is self-reported data, meaning our data is mostly biased, containinig outliers. Therefore, I will go over a few techniques for removing outliers in post.

Exploratory Data Analysis


Since the cleaning was more tedious I created external functions do handle this processes. In addition, I also created functions to handle transforming and plotting the data. I kept these functions in seperate files Cleaning_Functions.py and Plotting_Functions.py respecively so as to not clutter the post. We import these functions along with other basic libraries (Pandas, Matplotlib and Seaborn) as well as read in the data file below:

In [1]:
import warnings
warnings.filterwarnings('ignore')

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set()
from Cleaning_Functions import (initial_clean, 
                                convert_GeoPandas_to_Bokeh_format,
                                group_property_types)
                                
from Plotting_Functions import (plot_years_built, 
                                make_interactive_choropleth_map)

Here is we specifify a few datatypes as integers while reading in the Excel using the read_excel function from Pandas

In [2]:
df_2016 = pd.read_excel("data/nyc_benchmarking_disclosure_data_reported_in_2016.xlsx",
                       converters={'Street Number':int, 
                                   'Zip Code':int,
                                   'Year Build':int,
                                   'ENERGY STAR Score':int})

There are about 13,233 buildings with different types of energy usage, emissions and other information. I'll drop a bunch of these features and only keep the following,

  • Reported NYC Building Identification Numbers : [BINs]
  • NYC Borough, Block and Lot : [BBL]
  • Street Number : [Street_Number]
  • Street Name : [Street_Name]
  • Zip Code : [Zip_Code]
  • Borough : [Borough]
  • Year Built : [Year_Built]
  • DOF Benchmarking Status :[Benchmarking_Status]
  • Site EUI (kBtu/ft$^{2}$) : [Site_Eui]
  • Natural Gas Use [N(kBtu) : [Nat_Gas]
  • Electricity Use (kBtu): [Elec_Use]
  • Total GHG Emissions (Metric Tons CO2e) : [GHG]
  • ENERGY STAR Score : [Energy_Star]
  • Water Use (All Water Sources) (kgal) : [Water_Use]

The terms in the square brackets are the column names used in the dataframe. In addition, we must do some basic feature engineering. The reported data gives us the metrics (NAT_Gas, Elec_Use, GHG, Water_Use) in terms of total volume. Using these metrics in comparing buildings of different sizes is not a fair comparison. In order to compare them fairly we must standardize these metrics by dividing by the square footage of the buildings giving us each metrics' intensity. We therefore have the following features,

  • Nautral Gas Use Intensity (kBtu/ft$^{2}$) : [NGI]
  • Electricty Use Intensity (kBtu/ft$^{2}$) : [EI]
  • Water Use Intensity (kga/ft$^2$) : [WI]
  • Total GHG Emissions Intensity (Metric Tons CO2e / ft$^2$) : [GHGI]
  • Occupancy Per Square Foot (People / ft$^2$) : [OPSQFT]
  • Age (years)

I wrote a basic function called initial_clean(). to clean the data create the additional features. We call it on our dataset and then get some basic statistics about the data:

In [3]:
df_2016_2 = initial_clean(df_2016)
temp_cols_to_drop = ['BBL','Street_Number','Zip_Code','Occupancy']

df_2016_2.drop(temp_cols_to_drop, axis=1)\
         .describe()
Out[3]:
Energy_Star Site_EUI Nat_Gas Elec_Use GHG Water_Use NGI EI WI GHGI OPSFT Age
count 9535.000000 11439.000000 1.008700e+04 1.142500e+04 1.147800e+04 7.265000e+03 9870.000000 11206.000000 7261.000000 11258.000000 11311.000000 11531.000000
mean 57.735711 525.733377 2.520461e+07 8.201496e+06 6.952577e+03 2.579751e+04 137.705639 54.266179 0.161268 0.031272 0.001065 67.857168
std 30.143817 10120.105154 1.194068e+09 1.214643e+08 1.692231e+05 5.860239e+05 7512.527146 1210.530111 2.053453 0.571378 0.000536 30.263637
min 1.000000 0.000000 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 -2.000000
25% 34.000000 65.300000 8.915501e+05 1.045702e+06 3.420250e+02 2.661700e+03 7.324853 13.682696 0.028523 0.004308 0.000629 50.000000
50% 63.000000 82.400000 4.067600e+06 1.885996e+06 5.198000e+02 4.745600e+03 46.268145 18.482229 0.046098 0.005455 0.001075 76.000000
75% 83.000000 103.000000 6.919267e+06 4.513704e+06 9.394500e+02 8.057900e+03 68.036285 30.716894 0.073287 0.007003 0.001525 90.000000
max 100.000000 801504.700000 1.101676e+11 1.047620e+10 1.501468e+07 4.385740e+07 737791.764249 84461.681703 98.340480 39.190314 0.001999 417.000000

The above table is only a summary of the numrical data in the dataframe. Just looking at the count column we can immediately see that there are a lot of missing valus in this data. This tells me that this data will be rather messy with many columns having NaNs or missing values.

It also looks like there is a lot of variation within this dataset. Just looking at the Site_EUI statistic, the 75th percentile is is 103 (kBtu/ft²), but the max is 801,504.7 (kBtu/ft²). This probably due to the number of different types of buildings in the city, as well as the fact that contains outliers.

The next thing I would like to see is how many of the buildings in NYC are passing the Benchmarking Submission Status:

In [4]:
plt.figure(figsize=(10,4))
df_2016_2['Benchmarking_Status'].value_counts()\
                                .plot(kind='bar',
                                      fontsize=12,
                                      rot=0)
    
plt.title('DOF Benchmarking Submission Status',fontsize=14)
plt.ylabel('count',fontsize=12)
Out[4]:
Text(0, 0.5, 'count')

Most buildings are in compliance with the Department of Finance Benchmarking standards. Let's take a look at the violators:

In [6]:
Violators = df_2016_2.query("Benchmarking_Status == 'In Violation' ")
Violators.head()
Out[6]:
BBL BINs Street_Number Street_Name Zip_Code Borough Benchmarking_Status Property_Type Year_Built Occupancy ... Nat_Gas Elec_Use GHG Water_Use NGI EI WI GHGI OPSFT Age
11978 2.051410e+09 NaN 300 BAYCHESTER AVENUE 10475 Bronx In Violation NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
11979 3.088400e+09 NaN 3939 SHORE PARKWAY 11235 Brooklyn In Violation NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
11980 3.088420e+09 NaN 2824 PLUMB 3 STREET 11235 Brooklyn In Violation NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
11981 2.051411e+09 NaN 2100 BARTOW AVENUE 10475 Bronx In Violation NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
11982 2.051410e+09 NaN 312 BAYCHESTER AVENUE 10475 Bronx In Violation NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 22 columns

There's not much we can learn from this, if we can look to see if certain zip codes have more buildings in violation. First thing we do is group by the zip codes and count them to get the number of violations per zip code:

In [7]:
zips_df = Violators.groupby('Zip_Code')['Zip_Code'].size()\
                   .reset_index(name='counts')

Now we want to visualize the the number of violators per zip code. To make things interesting we will create an interactive choropleth map using the Bokeh Library. Bokeh is a great vizualization tool that I have used in the past. We get the shapes for New York City zip codes as a geojson file from this site. The geojson file can be read into a dataframe using GeoPandas.

In [8]:
import geopandas as gpd
gdf = gpd.read_file("data/nyc-zip-code-tabulation-areas-polygons.geojson")

# GeoPandas doesn't allow users to convert the datatype while reading it in so we do it here
gdf["postalCode"] = gdf["postalCode"].astype(int)

We can see the basic contents of the GeoPandas dataframe:

In [9]:
gdf.head(2)
Out[9]:
OBJECTID postalCode PO_NAME STATE borough ST_FIPS CTY_FIPS BLDGpostal @id longitude latitude geometry
0 1 11372 Jackson Heights NY Queens 36 081 0 http://nyc.pediacities.com/Resource/PostalCode... -73.883573 40.751662 POLYGON ((-73.86942457284177 40.74915687096788...
1 2 11004 Glen Oaks NY Queens 36 081 0 http://nyc.pediacities.com/Resource/PostalCode... -73.711608 40.745366 POLYGON ((-73.71132911125308 40.74947450816085...

I noticed only a few of the zipcodes had actual names, so I wrote a script (getNeighborhoodNames.py) to scrape this website to obtain each neighborhood's name. I pickled the results so we could use them here:

In [10]:
zip_names = pd.read_pickle("data/neighborhoods.pkl")

We can attach them to our GeoPandas dataframe by joining them (on zip code),

In [11]:
gdf = gdf.drop(['PO_NAME'],axis=1)\
         .merge(zip_names, on="postalCode",how="left")\
         .fillna("")

Next, we'll left join our count of violators-per-zipcode zips_df to above dataframe and fill in the zip codes that do not have violations with zeros:

In [12]:
gdf= gdf.merge(zips_df, how="left", left_on="postalCode", right_on="Zip_Code")\
         .drop(["OBJECTID","Zip_Code"], axis=1)\
         .fillna(0)

gdf.head(2)   
Out[12]:
postalCode STATE borough ST_FIPS CTY_FIPS BLDGpostal @id longitude latitude geometry PO_NAME counts
0 11372 NY Queens 36 081 0 http://nyc.pediacities.com/Resource/PostalCode... -73.883573 40.751662 POLYGON ((-73.86942457284177 40.74915687096788... West Queens 5.0
1 11004 NY Queens 36 081 0 http://nyc.pediacities.com/Resource/PostalCode... -73.711608 40.745366 POLYGON ((-73.71132911125308 40.74947450816085... Southeast Queens 0.0

Now before we can use Bokeh to visualize our data we must first convert the GeoPandas dataframe to a format that Bokeh can work with. Since I already covered this in a previous blog post I won't go over the details, but here I used a slightly modified version of the function from that post:

In [13]:
bokeh_source = convert_GeoPandas_to_Bokeh_format(gdf)

Next we set bokeh io module to be in the notebook and use the function I wrote make_interactive_choropleth_map to create the in-notebook zipcode choropleth map:

In [15]:
from bokeh.io import output_notebook, show
output_notebook()

# We get the min and max of the number of violations to give the cloropleth a scale.
max_num_violations = zips_df['counts'].max()
min_num_violations = zips_df['counts'].min()

fig = make_interactive_choropleth_map(bokeh_source,
                                      count_var = "Number Of Violations",
                                      min_ct    = min_num_violations,
                                      max_ct    = max_num_violations)
show(fig)
Loading BokehJS ...

You can hover your mouse over the each of the zipcode and the map will display the neighborhood name and number of violations. From this we can see that Chelsea, Downtown Brooklyn and Long Island City neighborhood have the highes number of violations.

The fact that different neighborhoods have different numbers of violating buildings gives us the suspicion that the neighborhood may be correlated with the buildings energy usage, this could be because of building owners that are in voliation owning multiple buildings on a single lot or neighrborhood.

Now let's move back to analyzing the buidlings that are not in violation. First let's see the distributution of all buildings that are in different ranges of the Energy Star ratings:

In [16]:
bins = [0,10,20,30,40,50,60,70,80,90,100]

df_2016_2['Energy_Star'].value_counts(bins=bins)\
                        .sort_index()\
                        .plot(kind    = 'bar',
                              rot     = 35,
                              figsize = (10,4),
                              title   = 'Frequency of ENERGY STAR Ratings')
plt.ylabel('Frequency')
plt.xlabel('ENERGY STAR Score')
Out[16]:
Text(0.5, 0, 'ENERGY STAR Score')

We can see that the majority are within the 50-100 range, but a almost 1000 buildings have scores inbetween 0 and 10. Let's take a look at the distribution of building types. We will just take the top 10 most common building types for now..

In [17]:
df_2016_2['Property_Type'].value_counts()\
                          .head(10)\
                          .plot(kind     = 'bar',
                                figsize  = (10,4.5),
                                fontsize = 12,
                                rot      = 60)
        
plt.title('Frequency of building type', fontsize=13)
plt.xlabel('Building Type', fontsize=13)
plt.ylabel('Frequency', fontsize=13)
Out[17]:
Text(0, 0.5, 'Frequency')

The most common buildings in NYC are multifamily housing, then offices, other, hotels and somewhat suprisingly non-refrigerated warehouse space. I would have thought that there would be more schools and retail spaces than warehouses or dormitorites in New York City, but I don't know what the Primary BBL listing is.

Let's look at the Energy Star ratings of buildings across different building types, but first how many different building types are there? We can find this out,

In [18]:
print("Number of building types are: {}".format(len(df_2016_2['Property_Type'].unique())))
Number of building types are: 54

This is too many building types to visualize the Energy Star Score (Energy_Star) of each, we'll just look at just 5 building types, lumping the 54 into the categories into either:

  • Residential
  • Office
  • Retail
  • Storage
  • Other

I built a function to group the buildings into the 5 types above called clean_property_type(...) and we use it below to transform the Pandas Series:

In [19]:
Property_Type = df_2016_2.copy()
Property_Type['Property_Type'] = Property_Type['Property_Type'].apply(group_property_types)

Now we can look at the Energy_Star (score) of each of the grouped buildings types:

In [20]:
bins2 = [0,20,35,50,65,80,100]

Energy_Star_Scores = Property_Type.groupby(['Property_Type'])['Energy_Star']

Energy_Star_Scores.value_counts(bins=bins2)\
                  .sort_index()\
                  .plot(kind='bar',
                        figsize=(13,6),
                        fontsize=13)

plt.title('Frequency of Energy Star Score by building type',fontsize=14)
plt.xlabel('Building Type and Energy Star', fontsize=13)
plt.ylabel('Frequency', fontsize=13)
Out[20]:
Text(0, 0.5, 'Frequency')

Overall it looks like residential buildings have a lot more proportion of low Energy Star Scoring buildings when compared to office buildings. This is probably because there are much more older residential buildings than office spaces in New York City. We'll look at the distribution of the years in which builings of just properties of type: 'Multifamily Housing' and 'Office' were built:

In [21]:
plot_years_built(df_2016_2)

It seems like it's the opposite of what I thought, but the number of residential buildings is much higher and the majority were built right before and right after World War 2, as well as in the 2000s. The same is true about offices, however, without the uptick in the early 2000s.

Connecting To BigQuery


Let's focus on the multifamily housing and office buildings to see what we can find out about them since these make up the majority of buildings and may offer the best return on investment in terms of improving energy efficiency. Let's create our dataset focusing on the fields:

- Energy Star 
- Site Energy Usage Intensity (Site_EUI)
- Natural Gas Intensity (NGI)
- Eletricity Intensity (EI)
- Water Intensity (WI)
- Green House Gas Intensity (GHGI)
- Occupancy Per Sq Ft (OPSFT)
- Age 
- Residential (1 or 0 if true or false)

We choose these fields as they should be independent of size of the building and therefore comparable across buildings.

In [23]:
Buildings = df_2016_2[df_2016_2['Property_Type'].isin(['Office','Multifamily Housing'])]

Buildings = Buildings.merge(pd.get_dummies(Buildings["Property_Type"])[["Multifamily Housing"]]
                              .rename(columns={"Multifamily Housing":"Residential"}),
                            left_index=True,
                            right_index=True)
                            
columns   = ["Energy_Star", "Site_EUI", "NGI", "EI", "WI", 
             "GHGI", "OPSFT", "Age", "Residential"]

Buildings = Buildings[columns]

Now since I will be working on this project over a few days I write this data to table so I won't have to constantly load and clean it again and again. Since I'm using the Google Cloud Platform, I'll using BigQuery for storing my data. This requires that I have credentials. I used the google-auth-oauthlib package and followed the instructions here to create a credentials json file:

In [24]:
from google.oauth2 import service_account

credentials = service_account.Credentials\
                             .from_service_account_file('./derby.json')

Next I installed pandas-gbq for connecting Pandas and BigQuery and set my credentials and project:

In [25]:
import pandas_gbq 

pandas_gbq.context.credentials = credentials
pandas_gbq.context.project     = credentials.project_id

Then I created a table raw_data in the database db_gb using the to_gbq function:

In [46]:
pandas_gbq.to_gbq(Buildings, 
                  "db_gb.raw_data")

And were done! We can query the data from the BigQuery UI as shown below:

bigquery

Let's move onto removing outliers.

Firs thing we need to do is to get the data from BigQuery as a Pandas dataframe. We use the read_gbq function from pandas_gbq:

In [26]:
X = pandas_gbq.read_gbq("""SELECT 
                               Energy_Star,
                               Site_EUI,
                               NGI,
                               EI,
                               WI,
                               GHGI,
                               OPSFT,
                               Age,
                               Residential,
                            FROM 
                                db_gb.raw_data 
                            """)
Downloading: 100%|██████████| 9932/9932 [00:01<00:00, 5334.91rows/s]

We can then look at the distribution of values in the dataframe:

In [38]:
X.describe()
Out[38]:
Energy_Star Site_EUI NGI EI WI GHGI OPSFT Age Residential
count 8617.000000 9879.000000 8527.000000 9656.000000 6281.000000 9686.000000 9720.000000 9932.000000 9932.000000
mean 58.403505 495.677558 150.094551 41.164075 0.099990 0.028340 0.001078 68.944724 0.875856
std 29.968956 10494.559237 8081.609124 807.887239 0.852744 0.582040 0.000535 29.120410 0.329763
min 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -2.000000 0.000000
25% 35.000000 67.100000 7.182433 13.636592 0.031498 0.004417 0.000641 53.000000 1.000000
50% 63.000000 82.400000 48.764998 17.831709 0.047561 0.005447 0.001095 77.000000 1.000000
75% 84.000000 101.200000 68.696570 26.824484 0.073500 0.006836 0.001536 90.000000 1.000000
max 100.000000 801504.700000 737791.764249 65067.140501 52.143200 39.190314 0.001999 417.000000 1.000000

We can see again, large variations in the energy data, with most of it being between 0 and some fixed number with atleast one outlier. For example, the minimum age of a building is -2, which is absurd! We can also see from the varying "count" values that there are a significant number of missing values. Let's find out just how many buildings have atleast one missing value:

In [27]:
X_clean = X.dropna()

print("Total Buildings: {}".format(X.shape[0]))
print("Total Buildings without any missing data: {}".format(X_clean.shape[0]))
Total Buildings: 9932
Total Buildings without any missing data: 4880

About half of the buildings have missing data! We'll first deal with removing outliers and then after that work on imputing missing values in the next post.

Let's plot the correlation matrix to see how correlated are features are on the all the buildings. Note that we first have to normalize the data.

In [28]:
from sklearn.preprocessing import StandardScaler
scaler1 = StandardScaler()
Xs      = scaler1.fit_transform(X)
xs_df   = pd.DataFrame(Xs, columns=X.columns)

fig, ax = plt.subplots(figsize=(8,6))  
sns.color_palette("BuGn_r",)
sns.heatmap(xs_df.corr(),
            linewidths=.5,
            cmap="RdBu_r")
Out[28]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1ff6e1d0>

We can see that,

  • Natural gas usage is fairly strongly correlated to green house emission rates which makes sense.
  • Energy usage intensity is strongly correlated with natural gas intensity and greenhouse gas emissions. These make sense, since gas is a primary form of heating.

What doesn't make sense to me is that the energy star score is weakly correlated to any of the measures of energy, water or emissions. This is strange to me because a higher energy star score is supposed to reflect more efficient use of energy and water. Furthermore, the energy star score goes up (slightly) as the age increases which doesn't make sense as the I would expect older homes to be less energy efficient.

We can see how the results change when we only use building data that does not have missing values:

In [29]:
Xs_clean_df = pd.DataFrame(StandardScaler().fit_transform(X_clean), 
                           columns=X_clean.columns)

fig, ax = plt.subplots(figsize=(8,6))  
sns.color_palette("BuGn_r",)
sns.heatmap(Xs_clean_df.corr(),
            linewidths=.5,
            cmap="RdBu_r")
Out[29]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a20820898>