Analyzing and predicting Service Request Types in DC¶

The flow adopted in this notebook is as follows:

Read in the datasets using ArcGIS API for Python

Merge datasets

Construct model that predicts service type

How many requests does each neighborhood make?

What kind of requests does each neighborhood mostly make?

Next Steps

The datasets used in this notebook are the

City Service Requests in 2018
Neighborhood Clusters

These datasets can be found at opendata.dc.gov

We start by importing the ArcGIS package to load the data using a service URL

In [1]:

import arcgis
from arcgis.features import *

1.1 Read in service requests for 2018¶

Link to Service Requests 2018 dataset

In [2]:

requests_url = 'https://maps2.dcgis.dc.gov/dcgis/rest/services/DCGIS_DATA/ServiceRequests/MapServer/9'

requests_layer = FeatureLayer(requests_url)
requests_layer

Out[2]:

<FeatureLayer url:"https://maps2.dcgis.dc.gov/dcgis/rest/services/DCGIS_DATA/ServiceRequests/MapServer/9">

In [ ]:

# Extract all the data and display number of rows
requests_features = requests_layer.query()
print('Total number of rows in the dataset: ')
print(len(requests_features.features))

This dataset updates on runtime, hence the number of rows could vary each time.

In [ ]:

# store as dataframe
requests = requests_features.sdf

# View first 5 rows
requests.head()

1.2 Read in Neighborhood Clusters dataset¶

Link to this dataset

In [ ]:

neighborhood_url = 'https://maps2.dcgis.dc.gov/dcgis/rest/services/DCGIS_DATA/Administrative_Other_Boundaries_WebMercator/MapServer/17'

neighborhood_layer = FeatureLayer(neighborhood_url)
neighborhood_layer

In [ ]:

# Extract all the data and display number of rows
neighborhood_features = neighborhood_layer.query()
print('Total number of rows in the dataset: ')
print(len(neighborhood_features.features))

In [ ]:

# store as dataframe
neighborhood = neighborhood_features.sdf

# View first 5 rows
neighborhood.head()

We now merge the two datasets

In [ ]:

# Connect to the GIS
from arcgis.gis import GIS
gis = GIS('http://dcdev.maps.arcgis.com/', 'username', 'password')

In [9]:

# Perform spatial join between CBG layer and the service areas created for all time durations
requests_with_neighborhood = arcgis.features.analysis.join_features(requests_url, neighborhood_url, spatial_relationship='Intersects', output_name='serviceRequests_Neighborhood_DC_1')

In [10]:

requests_with_neighborhood.share(everyone=True)

Out[10]:

{'itemId': '21ecb944fdc3495ea23e8cee411d0e29', 'notSharedWith': []}

In [11]:

requests_with_neighborhood_url = str(requests_with_neighborhood.url)+'/0/'
layer = FeatureLayer(requests_with_neighborhood_url)
features = layer.query()
print('Total number of rows in the dataset: ')
print(len(features.features))

Total number of rows in the dataset: 
80957

In [12]:

merged = features.sdf
merged.head()

Out[12]:

	ADDDATE	CITY	DETAILS	INSPECTIONDATE	INSPECTIONFLAG	INSPECTORNAME	Join_Count	LATITUDE	LONGITUDE	MARADDRESSREPOSITORYID	...	STATE	STATUS_CODE	STREETADDRESS	TYPE	WARD	WEB_URL	XCOORD	YCOORD	ZIPCODE	SHAPE
0	1514882874000	WASHINGTON	jg - Not Found – Close SR	NaN	N	None	1	38.894965	-76.935862	19821.0	...	DC	CLOSED	4531 EADS STREET NE	Original	7	http://planning.dc.gov/	405564.01	136348.29	20019.0	{'x': 405564.0099999979, 'y': 136348.2899999991}
1	1514883836000	WASHINGTON	am - Collected – Close SR	NaN	N	None	1	38.966837	-77.077571	285265.0	...	DC	CLOSED	5727 WESTERN AVENUE NW	Original	3	http://planning.dc.gov/	393277.48	144327.59	20015.0	{'x': 393277.4799999967, 'y': 144327.58999999985}
2	1514877377000	WASHINGTON	None	NaN	N	None	1	38.909417	-77.040607	238156.0	...	DC	CLOSED	1750 P STREET NW	Original	2	http://planning.dc.gov/	396478.07	137951.41	20036.0	{'x': 396478.0700000003, 'y': 137951.41000000015}
3	1514882441000	WASHINGTON	Per T. Duckett 1-9-18. closed by A .Hedgeman 0...	1.515497e+12	Y	None	1	38.927450	-77.097581	224717.0	...	DC	CLOSED	2895 UNIVERSITY TERRACE NW	Original	3	http://planning.dc.gov/	391538.72	139956.98	20016.0	{'x': 391538.7199999988, 'y': 139956.98000000045}
4	1514882008000	WASHINGTON	Collect on 1-6-18 by A.Hill	NaN	N	None	1	38.862290	-76.989317	67895.0	...	DC	CLOSED	1319 MAPLE VIEW PLACE SE	Original	8	http://planning.dc.gov/	400927.19	132719.19	20020.0	{'x': 400927.1899999976, 'y': 132719.19000000134}

5 rows × 34 columns

3. Construct model that predicts service type¶

The variables used to build the model are:

City Quadrant

Neighborhood cluster

Ward (Geographical unit)

Organization acronym

Status Code

3.1 Data preprocessing¶

In [13]:

quads = ['NE', 'NW', 'SE', 'SW']
def generate_quadrant(x):
    '''Function that extracts quadrant from street address'''
    try:
        temp = x[-2:]
        if temp in quads:
            return temp
        else:
            return 'NaN'
    except:
        return 'NaN'

In [14]:

merged['QUADRANT'] = merged['STREETADDRESS'].apply(generate_quadrant)
merged['QUADRANT'].head()

Out[14]:

0    NE
1    NW
2    NW
3    NW
4    SE
Name: QUADRANT, dtype: object

In [15]:

merged['QUADRANT'].unique()

Out[15]:

array(['NE', 'NW', 'SE', 'NaN', 'SW'], dtype=object)

In [16]:

merged['CLUSTER'] = merged['NAME'].apply(lambda x: x[8:])
merged['CLUSTER'].head()

Out[16]:

0    30
1    10
2     6
3    13
4    28
Name: CLUSTER, dtype: object

In [17]:

merged['CLUSTER'] = merged['CLUSTER'].astype(int)

In [18]:

merged['ORGANIZATIONACRONYM'].unique()

Out[18]:

array(['DPW', 'DDOT', 'FEMS', 'DOH', 'OUC', 'DOEE', 'DMV', 'ORM',
       'DC-ICH', 'DDS'], dtype=object)

In [19]:

merged['STATUS_CODE'].unique()

Out[19]:

array(['CLOSED', 'OPEN'], dtype=object)

Let's extract the number of possible outcomes, i.e. length of the target variable and also take a look at the values

In [20]:

len(merged['SERVICETYPECODEDESCRIPTION'].unique())

Out[20]:

In [21]:

requests['SERVICETYPECODEDESCRIPTION'].unique()

Out[21]:

array(['SNOW', 'PEMA- Parking Enforcement Management Administration',
       'Toa-Street & Bridge Maintenance', 'Urban Forrestry',
       'SWMA- Solid Waste Management Admistration',
       'Transportation Operations Administration', 'SIOD',
       'Department of Transportation', 'Driver Vehicle Services',
       'Toa-Trans Sys Mnt-Signs', 'Toa- Trans Sys Mnt',
       'DOH- Department Of Health', 'Tru-311',
       'Transportation Policy & Planning Administration',
       'FEMS-Smoke Alarms', 'FEMS-Special Events',
       'Department of Energy and Environment', 'Adjudication Services',
       '311- Call Center', 'HOMYDRPR- How Is My Driving Program',
       'DC Interagency Council on Homelessness', '311- Emergencies',
       'Department of Disability Services'], dtype=object)

3.2 Model building¶

In [22]:

# Import necessary packages
from sklearn.preprocessing import *
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

In [23]:

# Convert categorical (text) fields to numbers
number = LabelEncoder()
merged['SERVICETYPE_NUMBER'] = number.fit_transform(merged['SERVICETYPECODEDESCRIPTION'].astype('str'))
merged['STATUS_CODE_NUMBER'] = number.fit_transform(merged['STATUS_CODE'].astype('str'))

In [24]:

# Extract desired fields
data = merged[['SERVICETYPECODEDESCRIPTION', 'SERVICETYPE_NUMBER', 'QUADRANT', 'CLUSTER', 'WARD', 'ORGANIZATIONACRONYM', 'STATUS_CODE', 'STATUS_CODE_NUMBER']]
data.reset_index(inplace=True)
data.head()

Out[24]:

	index	SERVICETYPECODEDESCRIPTION	SERVICETYPE_NUMBER	QUADRANT	CLUSTER	WARD	ORGANIZATIONACRONYM	STATUS_CODE
0	0	SWMA- Solid Waste Management Admistration	15	NE	30	7	DPW	CLOSED
1	1	SWMA- Solid Waste Management Admistration	15	NW	10	3	DPW	CLOSED
2	2	PEMA- Parking Enforcement Management Administr...	12	NW	6	2	DPW	CLOSED
3	3	SNOW	14	NW	13	3	DPW	CLOSED
4	4	SWMA- Solid Waste Management Admistration	15	SE	28	8	DPW	CLOSED

Let's binarize values in fields QUADRANT (4) and ORGANIZATIONACRONYM (8)

Wonder why are not doing it for CLUSTER? Appropriate nomenclature of adjacent clusters.

In [25]:

import pandas as pd
data = pd.get_dummies(data=data, columns=['QUADRANT', 'ORGANIZATIONACRONYM'])
data.head()

Out[25]:

	index	SERVICETYPECODEDESCRIPTION	SERVICETYPE_NUMBER	CLUSTER	WARD	STATUS_CODE	QUADRANT_NE	QUADRANT_NW	...	ORGANIZATIONACRONYM_DPW
0	0	SWMA- Solid Waste Management Admistration	15	30	7	CLOSED	1	0	...	1
1	1	SWMA- Solid Waste Management Admistration	15	10	3	CLOSED	0	1	...	1
2	2	PEMA- Parking Enforcement Management Administr...	12	6	2	CLOSED	0	1	...	1
3	3	SNOW	14	13	3	CLOSED	0	1	...	1
4	4	SWMA- Solid Waste Management Admistration	15	28	8	CLOSED	0	0	...	1

5 rows × 22 columns

In [26]:

# Extract input dataframe
model_data = data.drop(['SERVICETYPECODEDESCRIPTION', 'SERVICETYPE_NUMBER', 'STATUS_CODE'], axis=1)
model_data.head()

Out[26]:

	index	CLUSTER	WARD	QUADRANT_NE	QUADRANT_NW	QUADRANT_SE	ORGANIZATIONACRONYM_DPW
0	0	30	7	1	0	0	1
1	1	10	3	0	1	0	1
2	2	6	2	0	1	0	1
3	3	13	3	0	1	0	1
4	4	28	8	0	0	1	1

In [27]:

def handle_ward(x):
    accept = [range(0,8)]
    if x not in accept:
        return 0
    else:
        return x

In [28]:

model_data['WARD'] = model_data['WARD'].apply(handle_ward)

In [29]:

# Define independent and dependent variables
y = data['SERVICETYPE_NUMBER'].values
X = model_data.values

In [30]:

# Split data into training and test samples of 70%-30%
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = .3, random_state=522, stratify=y)

In [31]:

# n_estimators = number of trees in the forest
# min_samples_leaf = minimum number of samples required to be at a leaf node for the tree
rf = RandomForestClassifier(n_estimators=2500, min_samples_leaf=5, random_state=522)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
print(y_pred)

[15 15 12 ... 15 21 15]

In [32]:

print('Accuracy: ', accuracy_score(y_test, y_pred))

Accuracy:  0.6824769433465085

3.3 Alternate model, excluding the department codes¶

In [33]:

data = merged[['SERVICETYPECODEDESCRIPTION', 'SERVICETYPE_NUMBER', 'QUADRANT', 'CLUSTER', 'WARD', 'ORGANIZATIONACRONYM', 'STATUS_CODE', 'STATUS_CODE_NUMBER']]
data.reset_index(inplace=True)
data.head()

Out[33]:

	index	SERVICETYPECODEDESCRIPTION	SERVICETYPE_NUMBER	QUADRANT	CLUSTER	WARD	ORGANIZATIONACRONYM	STATUS_CODE
0	0	SWMA- Solid Waste Management Admistration	15	NE	30	7	DPW	CLOSED
1	1	SWMA- Solid Waste Management Admistration	15	NW	10	3	DPW	CLOSED
2	2	PEMA- Parking Enforcement Management Administr...	12	NW	6	2	DPW	CLOSED
3	3	SNOW	14	NW	13	3	DPW	CLOSED
4	4	SWMA- Solid Waste Management Admistration	15	SE	28	8	DPW	CLOSED

In [34]:

data1 = pd.get_dummies(data=data,columns=['QUADRANT'])
data1.head()

Out[34]:

	index	SERVICETYPECODEDESCRIPTION	SERVICETYPE_NUMBER	CLUSTER	WARD	ORGANIZATIONACRONYM	STATUS_CODE	QUADRANT_NE	QUADRANT_NW	QUADRANT_SE
0	0	SWMA- Solid Waste Management Admistration	15	30	7	DPW	CLOSED	1	0	0
1	1	SWMA- Solid Waste Management Admistration	15	10	3	DPW	CLOSED	0	1	0
2	2	PEMA- Parking Enforcement Management Administr...	12	6	2	DPW	CLOSED	0	1	0
3	3	SNOW	14	13	3	DPW	CLOSED	0	1	0
4	4	SWMA- Solid Waste Management Admistration	15	28	8	DPW	CLOSED	0	0	1

In [35]:

model_data1 = data1.drop(['SERVICETYPECODEDESCRIPTION', 'SERVICETYPE_NUMBER', 'STATUS_CODE', 'ORGANIZATIONACRONYM'], axis=1)
model_data1.head()

Out[35]:

	index	CLUSTER	WARD	QUADRANT_NE	QUADRANT_NW	QUADRANT_SE
0	0	30	7	1	0	0
1	1	10	3	0	1	0
2	2	6	2	0	1	0
3	3	13	3	0	1	0
4	4	28	8	0	0	1

In [36]:

model_data1['WARD'] = model_data1['WARD'].apply(handle_ward)

In [37]:

y = data['SERVICETYPE_NUMBER'].values
X = model_data1.values

In [38]:

# Split data into training and test samples of 70%-30%
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = .3, random_state=522, stratify=y)

In [39]:

# n_estimators = number of trees in the forest
# min_samples_leaf = minimum number of samples required to be at a leaf node for the tree
rf = RandomForestClassifier(n_estimators=2500, min_samples_leaf=5, random_state=522)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
print(y_pred)

[15 15 12 ... 15 15 15]

In [40]:

print('Accuracy: ', accuracy_score(y_test, y_pred))

Accuracy:  0.4862895256916996

A drop in accuracy from 68.39% to 48.78% demonstrates the importance of using the correct predictors.

4. How many requests does each neighborhood make?¶

In [41]:

# Count of service requests per cluster
cluster_count = merged.groupby('NAME').size().reset_index(name='counts')
cluster_count.head()

Out[41]:

	NAME	counts
0	Cluster 1	1991
1	Cluster 10	1707
2	Cluster 11	2475
3	Cluster 12	767
4	Cluster 13	1625

In [42]:

# merge with original file
neighborhood = pd.merge(neighborhood, cluster_count, on='NAME')
neighborhood.head()

Out[42]:

	NAME	NBH_NAMES	OBJECTID	Shape_Area	Shape_Length	TYPE	WEB_URL	SHAPE	counts
0	Cluster 39	Congress Heights, Bellevue, Washington Highlands	1	4.886463e+06	10711.668010	Original	http://planning.dc.gov/	{'rings': [[[-8570934.978117127, 4699521.51243...	2360
1	Cluster 38	Douglas, Shipley Terrace	2	2.367958e+06	8229.486324	Original	http://planning.dc.gov/	{'rings': [[[-8568786.426828014, 4700618.41227...	733
2	Cluster 36	Woodland/Fort Stanton, Garfield Heights, Knox ...	3	1.119573e+06	4746.344457	Original	http://planning.dc.gov/	{'rings': [[[-8568124.617559846, 4701733.64556...	378
3	Cluster 27	Near Southeast, Navy Yard	4	1.619167e+06	7286.968902	Original	http://planning.dc.gov/	{'rings': [[[-8570182.535376322, 4704085.08115...	480
4	Cluster 32	River Terrace, Benning, Greenway, Dupont Park	5	4.286254e+06	11251.012821	Original	http://planning.dc.gov/	{'rings': [[[-8564654.618529493, 4705921.46259...	1285

In [43]:

temp = neighborhood.sort_values(['counts'], ascending=[False])
temp[['NAME', 'NBH_NAMES', 'counts']]

Out[43]:

	NAME	NBH_NAMES	counts
33	Cluster 2	Columbia Heights, Mt. Pleasant, Pleasant Plain...	5291
30	Cluster 25	Union Station, Stanton Park, Kingman Park	5277
20	Cluster 18	Brightwood Park, Crestwood, Petworth	5137
13	Cluster 6	Dupont Circle, Connecticut Avenue/K Street	4629
38	Cluster 26	Capitol Hill, Lincoln Park	3876
5	Cluster 8	Downtown, Chinatown, Penn Quarters, Mount Vern...	3763
32	Cluster 21	Edgewood, Bloomingdale, Truxton Circle, Eckington	3554
21	Cluster 11	Friendship Heights, American University Park, ...	2475
23	Cluster 17	Takoma, Brightwood, Manor Park	2467
6	Cluster 5	West End, Foggy Bottom, GWU	2445
29	Cluster 34	Twining, Fairlawn, Randle Highlands, Penn Bran...	2429
14	Cluster 3	Howard University, Le Droit Park, Cardozo/Shaw	2412
0	Cluster 39	Congress Heights, Bellevue, Washington Highlands	2360
9	Cluster 7	Shaw, Logan Circle	2270
12	Cluster 4	Georgetown, Burleith/Hillandale	2243
16	Cluster 33	Capitol View, Marshall Heights, Benning Heights	2145
11	Cluster 23	Ivy City, Arboretum, Trinidad, Carver Langston	2110
34	Cluster 22	Brookland, Brentwood, Langdon	2066
8	Cluster 31	Deanwood, Burrville, Grant Park, Lincoln Heigh...	1999
31	Cluster 1	Kalorama Heights, Adams Morgan, Lanier Heights	1991
15	Cluster 9	Southwest Employment Area, Southwest/Waterfron...	1965
25	Cluster 10	Hawthorne, Barnaby Woods, Chevy Chase	1707
22	Cluster 19	Lamont Riggs, Queens Chapel, Fort Totten, Plea...	1628
17	Cluster 13	Spring Valley, Palisades, Wesley Heights, Foxh...	1625
18	Cluster 20	North Michigan Park, Michigan Park, University...	1427
37	Cluster 15	Cleveland Park, Woodley Park, Massachusetts Av...	1322
4	Cluster 32	River Terrace, Benning, Greenway, Dupont Park	1285
36	Cluster 14	Cathedral Heights, McLean Gardens, Glover Park	1070
45	Cluster 45	National Mall, Potomac River	952
35	Cluster 24	Woodridge, Fort Lincoln, Gateway	941
7	Cluster 30	Mayfair, Hillbrook, Mahaning Heights	778
19	Cluster 12	North Cleveland Park, Forest Hills, Van Ness	767
1	Cluster 38	Douglas, Shipley Terrace	733
24	Cluster 16	Colonial Village, Shepherd Park, North Portal ...	715
26	Cluster 28	Historic Anacostia	678
27	Cluster 35	Fairfax Village, Naylor Gardens, Hillcrest, Su...	642
28	Cluster 37	Sheridan, Barry Farm, Buena Vista	524
3	Cluster 27	Near Southeast, Navy Yard	480
2	Cluster 36	Woodland/Fort Stanton, Garfield Heights, Knox ...	378
10	Cluster 29	Eastland Gardens, Kenilworth	191
43	Cluster 44	Joint Base Anacostia-Bolling	77
40	Cluster 41	Rock Creek Park	51
44	Cluster 46	Arboretum, Anacostia River	41
42	Cluster 43	Saint Elizabeths	26
39	Cluster 40	Walter Reed	12
41	Cluster 42	Observatory Circle	3

In [44]:

# Viewing the map
search_result = gis.content.search("Neighborhood_Service_Requests")
search_result[0]

Out[44]:

Neighborhood_Service_Requests

Web Map by mmajumdar_dcdev
Last Modified: January 22, 2018
0 comments, 0 views

5. What kind of requests does each neighborhood mostly make?¶

In [45]:

import scipy.stats
merged.columns

Out[45]:

Index(['ADDDATE', 'CITY', 'DETAILS', 'INSPECTIONDATE', 'INSPECTIONFLAG',
       'INSPECTORNAME', 'Join_Count', 'LATITUDE', 'LONGITUDE',
       'MARADDRESSREPOSITORYID', 'NAME', 'NBH_NAMES', 'OBJECTID',
       'ORGANIZATIONACRONYM', 'PRIORITY', 'RESOLUTIONDATE', 'SERVICECALLCOUNT',
       'SERVICECODE', 'SERVICECODEDESCRIPTION', 'SERVICEDUEDATE',
       'SERVICEORDERDATE', 'SERVICEORDERSTATUS', 'SERVICEREQUESTID',
       'SERVICETYPECODEDESCRIPTION', 'STATE', 'STATUS_CODE', 'STREETADDRESS',
       'TYPE', 'WARD', 'WEB_URL', 'XCOORD', 'YCOORD', 'ZIPCODE', 'SHAPE',
       'QUADRANT', 'CLUSTER', 'SERVICETYPE_NUMBER', 'STATUS_CODE_NUMBER'],
      dtype='object')

In [46]:

df = merged[['NAME', 'SERVICECODEDESCRIPTION']]

In [ ]:

# Extract the most frequently occuring service request type, and its count
df1 = df.groupby('NAME').agg(lambda x: scipy.stats.mode(x)[0][0])
df2 = df.groupby('NAME').agg(lambda x: scipy.stats.mode(x)[1][0])

In [48]:

df1.reset_index(inplace=True)
df2.reset_index(inplace=True)
df2 = df2.rename(columns={'SERVICECODEDESCRIPTION':'SERVICECODEDESCRIPTION_COUNT'})

In [49]:

# merge the two datasets
final_df = pd.merge(df1, df2, on='NAME')
final_df.head()

Out[49]:

	NAME	SERVICECODEDESCRIPTION	SERVICECODEDESCRIPTION_COUNT
0	Cluster 1	Parking Enforcement	383
1	Cluster 10	Bulk Collection	342
2	Cluster 11	Parking Meter Repair	863
3	Cluster 12	Roadway Signs	111
4	Cluster 13	Pothole	254

In [50]:

# merge it with neighborhood clusters
neighborhood_data = pd.merge(neighborhood, final_df, on='NAME')

In [51]:

# view the map
search_result = gis.content.search("Neighborhood_Service_DC")
search_result[0]

Out[51]:

Neighborhood_Service_DC

Web Map by mmajumdar_dcdev
Last Modified: April 05, 2018
0 comments, 0 views

	index	CLUSTER	WARD	QUADRANT_NE	QUADRANT_NW	QUADRANT_SE	ORGANIZATIONACRONYM_DPW
0	0	30	7	1	0	0	1
1	1	10	3	0	1	0	1
2	2	6	2	0	1	0	1
3	3	13	3	0	1	0	1
4	4	28	8	0	0	1	1

	index	CLUSTER	WARD	QUADRANT_NE	QUADRANT_NW	QUADRANT_SE	ORGANIZATIONACRONYM_DPW
0	0	30	7	1	0	0	1
1	1	10	3	0	1	0	1
2	2	6	2	0	1	0	1
3	3	13	3	0	1	0	1
4	4	28	8	0	0	1	1

ArcGIS API for Python Sample Notebooks

Analyzing and predicting Service Request Types in DC¶

1.1 Read in service requests for 2018¶

1.2 Read in Neighborhood Clusters dataset¶

3. Construct model that predicts service type¶

3.1 Data preprocessing¶

3.2 Model building¶

3.3 Alternate model, excluding the department codes¶

4. How many requests does each neighborhood make?¶

5. What kind of requests does each neighborhood mostly make?¶

	index	CLUSTER	WARD	QUADRANT_NE	QUADRANT_NW	QUADRANT_SE	ORGANIZATIONACRONYM_DPW
0	0	30	7	1	0	0	1
1	1	10	3	0	1	0	1
2	2	6	2	0	1	0	1
3	3	13	3	0	1	0	1
4	4	28	8	0	0	1	1