PyCon 2012: Python for data lovers: explore it, analyze it, map it

Python for Open Data Lovers:
Explore It, Analyze It, Map It

Jackie Kazil Dana Bauer
@jackiekazil @geography76

Saturday, March 10, 2012

Where are we going?
• open data everywhere
• a data swiss army knife
• ﬁnding network patterns
• ﬁnding spatial patterns
• which stories to pursue? moving beyond
data analysis


• Data.gov

• OpenDataPhilly

• DC Data Catalog

• DataSF

• Chicago Data Portal

• NYC Open Data

• London Datastore


assembly member expenses
bicycle lanes
city purchase orders
dialysis centers
elevation data
filming locations
Google Transit Feed Specification (GTFS)
historical photos
influenza rates
judicial districts
Key Stage 2 test results by free school meal eligibility
land cover
monthly calls to Human Services Agency switchboard operators
neighborhood health clinics
Oyster ticket stop locations
political districts
quality of life indicators
restaurant inspections
sewer lines
traffic counts
utility excavation and paving five-year plan
violent crime incidents
ward offices
youth centers
zoning

**real-time parking availability and pricing**


http://bit.ly/DCdatafail


• What are DC agencies spending money on?
• How much are they spending?
• What are the relationships between
businesses and agencies?

• Where are these businesses located?


swiss army knife

• csvkit: http://csvkit.readthedocs.org/

• a set of Python utilities for working with csv

• meant to replace csv module

• pip install csvkit (no issues!)


$ csvcut -n purchase2011_cleaned.csv
1: PO_NUMBER
2: AGENCY_NAME
3: NIGP_DESCRIPTION
4: PO_TOTAL_AMOUNT
5: ORDER_DATE
6: SUPPLIER
7: SUPPLIER_FULL_ADDRESS

! ! !


$ csvcut -c 2,6 purchase2011_cleaned.csv | csvstat
1. AGENCY_NAME
! <type 'unicode'>
! Nulls: False
! Unique values: 85
! 5 most frequent values:
! ! DISTRICT OF COLUMBIA PUBLIC SCHOOLS:!2410
! ! STATE SUPERINTENDENT OF EDUCATION (OSSE):! 1340
! ! DEPARTMENT OF HEALTH:! 895
! ! OFFICE OF CHIEF TECHNOLOGY OFFICER:! 786
! ! OFF PUBLIC ED FACILITIES MODERNIZATION:!722
! Max length: 40
2. SUPPLIER
! <type 'unicode'>
! Nulls: False
! Unique values: 4357
! 5 most frequent values:
! ! OST, INC.:! 841
! ! DELL COMPUTER CORP.:! 366
! ! AMERICAN EXPRESS COMPANY:! 282
! ! MVS, INC.:! 176
! ! CAPITAL SERVICES AND SUPPLIES:! 167
! Max length: 52

Row count: 16075

! ! !


$ csvgrep -c 6 -r ^MAYA purchase2011_cleaned.csv

PO_NUMBER,AGENCY_NAME,NIGP_DESCRIPTION,PO_TOTAL_AMOUNT,ORDER_DATE,SUPPLIER,SUPPLIER_FULL_ADDRESS
PO352244,STATE SUPERINTENDENT OF EDUCATION (OSSE),EDUCATION AND TRAINING CONSULTING 38,408644.73,01/04/2011,MAYA ANGELOU
PCS,"1851 9TH STREET NW, WASHINGTON, DC, 20001"
PO352920,PUBLIC CHARTER SCHOOLS,SCHOOL OPERATION AND MANAGEMENT SERVICES 71,2205630.13,01/11/2011,MAYA ANGELOU PCS,"1851
9TH STREET NW, WASHINGTON, DC, 20001"
PO356426,STATE SUPERINTENDENT OF EDUCATION (OSSE),FINANCIAL SERVICES (NOT OTHERWISE CLASSIFIED)
49,999891,02/23/2011,MAYA ANGELOU PCS,"1851 9TH STREET NW, WASHINGTON, DC, 20001"
PO356632,STATE SUPERINTENDENT OF EDUCATION (OSSE),PROFESSIONAL SERVICES (NOT OTHERWISE CLASSIFIED)
58,187200,02/25/2011,MAYA ANGELOU PCS,"1851 9TH STREET NW, WASHINGTON, DC, 20001"
PO359961,PUBLIC CHARTER SCHOOLS,SCHOOL OPERATION AND MANAGEMENT SERVICES 71,1753238,04/12/2011,MAYA ANGELOU PCS,"1851
PO351462-V2,STATE SUPERINTENDENT OF EDUCATION (OSSE),EDUCATIONAL RESEARCH SERVICES 19,152229.95,05/05/2011,MAYA ANGELOU
PO366839,PUBLIC CHARTER SCHOOLS,SCHOOL OPERATION AND MANAGEMENT SERVICES 71,2767027,07/12/2011,MAYA ANGELOU PCS,"1851
PO365094-V2,STATE SUPERINTENDENT OF EDUCATION (OSSE),YOUTH CARE SERVICES 95,98092.35,08/15/2011,MAYA ANGELOU PCS,"1851
PO370948,STATE SUPERINTENDENT OF EDUCATION (OSSE),YOUTH CARE SERVICES 95,45736.58,08/25/2011,MAYA ANGELOU PCS,"1851 9TH
STREET NW, WASHINGTON, DC, 20001"
PO361027-V5,STATE SUPERINTENDENT OF EDUCATION (OSSE),EDUCATION AND TRAINING CONSULTING 38,29424.86,09/06/2011,MAYA
ANGELOU PCS,"1851 9TH STREET NW, WASHINGTON, DC, 20001"
PO374132,STATE SUPERINTENDENT OF EDUCATION (OSSE),YOUTH CARE SERVICES 95,9000,09/28/2011,MAYA ANGELOU PCS,"1851 9TH
PO377919,STATE SUPERINTENDENT OF EDUCATION (OSSE),YOUTH CARE SERVICES 95,491663.6,10/25/2011,MAYA ANGELOU PCS,"1851 9TH
PO383965,STATE SUPERINTENDENT OF EDUCATION (OSSE),YOUTH CARE SERVICES 95,294690.57,12/22/2011,MAYA ANGELOU PCS,"1436 U
STREET, NW SUITE 203, WASHINGTON, DC, 20009"
! ! !

$ csvcut -c 4,2,6,5 purchase2011_cleaned.csv | csvsort -r | head -n
20 | csvlook
------------------------------------------------------------------------------------------------------------
| PO_TOTAL_AMOUNT | AGENCY_NAME | SUPPLIER | ORDER_DATE |
------------------------------------------------------------------------------------------------------------
| 154133337.02 | DEPARTMENT OF TRANSPORTATION | SKANSKA-FACCHINA JV | 2011-11-10 |
| 62677473.88 | DEPARTMENT OF REAL ESTATE SERVICES | EEC OF DC INC-FORRESTER CONSTR | 2011-09-22 |
| 31809425.48 | DEPARTMENT OF HEALTH | DEFENSE LOGISTIC AGENCY | 2011-09-08 |
| 23600580.0 | DEPARTMENT OF CORRECTIONS | UNITY HEALTH CARE, INC. | 2011-10-24 |
| 23538552.0 | DEPARTMENT OF REAL ESTATE SERVICES | EEC-FORRESTER ANACOSTIA | 2011-11-08 |
| 22375314.45 | DEPARTMENT OF CORRECTIONS | CORRECTIONS CORPORATION OF | 2011-05-25 |
| 21450000.04 | DEPARTMENT OF HUMAN SERVICES | THE COMMUNITY PARTNERSHIPHOME | 2011-08-18 |
| 20813348.99 | DEPARTMENT OF REAL ESTATE SERVICES | THE JOHN AKRIDGE CO | 2011-06-28 |
| 20622000.0 | DEPARTMENT OF TRANSPORTATION | W M SCHLOSSER CO INC | 2011-08-29 |
| 19824914.0 | DEPARTMENT OF CORRECTIONS | CORRECTIONS CORPORATION OF | 2011-10-24 |
| 18000000.0 | DEPARTMENT OF HEALTH | DC PRIMARY CARE ASSOCIATION | 2011-03-10 |
| 17000000.0 | DEPARTMENT OF HEALTH | CHILDRENS NATIONAL MEDICAL CTR | 2011-11-25 |
| 16850000.0 | DEPUTY MAYOR FOR ECONOMIC DEVELOPMENT | 2 M STREET REDEVELOPMENT LLC | 2011-09-29 |
| 14206937.0 | PUBLIC CHARTER SCHOOLS | FRIENDSHIP PCS | 2011-07-12 |
| 13862557.44 | MUNICIPAL FACILITIES: NON-CAPITAL | US SECURITY ASSOCIATES, INC. | 2011-10-07 |
| 13800000.0 | DISTRICT DEPARTMENT OF THE ENVIRONMENT | VERMONT ENERGY INVESTMENT CORP | 2011-10-04 |
------------------------------------------------------------------------------------------------------------

! ! !


Social Network Analysis

“Social network analysis is focused on
uncovering the patterning of people's
interaction.”
- http://www.insna.org/sna/what.html


99th House

President: Reagan
House majority: Democrats
Years: 1985, 1986


107th House

President: Bush
House majority: Republicans
Years: 2001, 2002


108th House

President: Bush
Years: 2003, 2004


109th House

President: Bush
Years: 2005, 2006


110th House

President: Bush
Years: 2007, 2008


111th House

President: Obama
Years: 2009, 2010


CSV to network
import networkx as nx

G = nx.Graph()
node_edgelist = []

# grab edges
for row in csv_ﬁle:
node_edgelist.append((n,e))

# create edges
for f in node_edgelist:
for t in node_edgelist:
if t != f:
add_edge_or_weight(G, f[0], t[0])


Centrality Analysis (networkx)
Degree - nx.degree(G)
# of connections; More connections = more important

Closeness centrality
nx.closeness_centrality(G)
Distance to all other nodes; Closer = more important

Betweenness centrality
nx.betweenness_centrality(G)
Based on the shortest path of info control

Page rank
nx.pagerank(G)
Node gains importance via the importance around him


Digi Docs Inc Document Mangers (Dallas)
“Offers software that generates loan documents for electronic delivery.”

Iron Mountain (Mountain View)
“Iron Mountain provides information management services that help organizations
lower the costs, risks and inefficiencies of managing their physical and digital data.”

MVS, Inc. (Washington, DC)
“MVS Consulting is an 8(a) STARS II, HUBZone, LSDBE, CBE, and MBE IT
Solutions company that provides IT solutions to Federal, State and Local
Government Agencies.”

MDM OFFICE SYSTEMS INC (Washington, DC)
"Standard Office Supply - Office Supplies, Furniture Dealer, Educational Products,
Breakroom Supplies, Imaging Supplies, and Coffee Services"

Capital Services and Supplies (Washington, DC)
“CSSI is an office solutions firm located in Washington, DC since 1980. CSSI’s
goods and services are available to commercial, government, and educational
institutions throughout the continental United States.”


Not included in previous slide...

United States Postal Service
&
Dell Computer Corp


Visual the network
pos=nx.spring_layout(G,iterations=100)
plot.figure(1,figsize=(15,15))
plt.axis('off')

nx.draw_networkx_nodes(
G,
pos,node_size=100,
alpha=1,
node_color='g'
)

nx.draw_networkx_edges(G,pos,alpha=0.2)
plot.savefig('graph.png')


Visual the network


Trimming nodes

g2 = G.copy()
d = nx.degree(g2)
for n in g2.nodes():
if d[n] <= degree: g2.remove_node(n)
return g2


Degree Distribution
d=nx.degree(G)
plot.ﬁgure(1,ﬁgsize=(15,10))
h=plot.hist(d.values(),100)


Degree Distribution


Trimmed nodes


Adding labels


nx.draw_networkx_labels
(g3,pos,alpha=1)
nx.draw_networkx_edges
(g3,pos,alpha=0.05)


Maps to maps


Spatial is special

• spatial data = attributes, location, time
• mappable!
• spatial data must be referenced in space
• Tobler’s First Law of Geography


Spatial analysis
• large data sets a smaller amount of
meaningful information
• exploratory (ESDA)
• spatial statistics
• mathematical modeling and prediction of
spatial processes


Techniques

• point pattern analysis -- hot spots, k
density, nearest neighbor
• spatial interpolation -- kriging
• spatial regression -- ordinary least squares,
geographically weighted regression


PySAL

• GeoDa Center at ASU

• Python library for spatial analysis, with modules for
exploratory spatial data analysis, spatial
econometrics, and location modeling

• http://code.google.com/p/pysal/

• requires NumPy, SciPy


PySAL
• developers looking for spatial analytical methods
to incorporate in application development

• analysts working on projects that require custom
scripting

• looking for a user-friendly GUI? Try STARS,
GeoDA, GeoDASpace.

• want to integrate into a powerful GIS? Look for
plug-ins for ArcGIS & QGIS.


Next steps

• quantify clusters in city, region, nation

• examine clusters along networks, business
corridors

• create beautiful, interactive maps and charts to
allow users to explore spending patterns on their
own


From data analysis to stories


Which stories would we go
after?
• construction contracts
• funding to charter schools
• health care costs in prisons
• local vs. regional vs. national purchases
• technology services -- look for overlap


Want to learn more?
The SAGE Handbook of
Spatial Analysis
eds. A. Stewart Fotheringham and
Peter A. Rogerson

Interactive Spatial Data
Analysis
Trevor Bailey and Tony Gatrell

Geographic Information
Analysis
David O’Sullivan and David Unwin

PySAL
Luc Anselin, GeoDA Center
Arizona State University Mia, age 3, geographer in training


And even more?
NetworkX tutorial
http://networkx.lanl.gov/
networkx_tutorial.pdf

UCD Dublin summer course
http://mlg.ucd.ie/summer

Social Network Analysis for
Startups (O'Reilly Media)
http://shop.oreilly.com/product/
0636920020424.do


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