Data Tactics Analytics Brown Bag (Aug 22, 2013)

DT Brown Bag: A Primer in Analytics
WELCOME!
R2 = 500; p<marty’s 1mile time
asymptotically approaching perfect
Thursday, August 22, 13

Outline
•EAT, Guten Appetit, Bon appetit, Buen apetito, Buon appetito!
•Words from the VP
•Why this brown-bag?
•Analytics Services:
•Team Introduction; About YOU!
•Why Analytics!?
•Philosophy...
•Case Studies:
•Case Study (Nathan D.)
•Localview (Marty A.)
•Case Study (me)
•Core Values: Analytical Insights
•On the horizon...

Why this brown bag??
Learning [close] at a pace similar to the pace at which we learn.
Learning and Educating from/to PMs, SWE, and OPs.
PM: Provide insights from FRIs/RFPs.
PM: Atmospherics from our costumers.
SWE: Accessing data spaces.
SWE: Integrating algorithms.
OP: How do you best consume the outputs of models?
OP: What models are best to present to OPs?
PM: Program Managers, SWE: Software Engineers, OP: Operators

ISW
USMA
DARPA%...%
Why this brown bag??

Data Tactics Analytics Practice
The Team:
(Nathan D., Shrayes R., David P., Adam VE., Andrew T., Geoffrey B., Rich H.)
Graduates from top universities...
Degrees include:
mathematics, computer science, aeronautical engineering,
astrophysics, electrical engineering, mechanical engineering, statistics,
social science(s).
Base competencies (horizontals): Clustering, Association Rules,
Regression, Naive Bayesian Classiﬁer, Decision Trees, Time-Series,
Text Analysis.
Going beyond the base (verticals)...

ABOUT YOU:
28 confirmed, 18 webex, 14 tentative (n:60 represent > 25% of the company)
21 confirmed within the first 60 minutes....
Monsee Wood & Steve Moccio 1st
Charles Fuller & Lenesto Page Last
Chris Zilligen: 3,120 (Longest resume)
Catherine Schymanski: 284 (shortest resume)
Linguistic Standard:
Jack Gustafson (FK: -126)
Shrayes Ramesh (FK: -38)
...analytics team below the company average!! :)

Horizontals & Verticals
Clustering || Regression || Decision Trees || Text Analysis
Association Rules || Naive Bayesian Classiﬁer || Time Series Analysis
econom
etricsspatialeconom
etrics
graph
theory
algorithm
s
astrophysicaltim
e-series
analysis
path
planning
algorithm
s
bayesian
statistics
constrained
optim
izations
num
ericalintegration
techniques
PCA
G
LM
hierarchicalm
odels
IRT
DLISA
latentclass
analysis
structuralequation
m
odeling
m
ixture
m
odels
SVM
m
axent
CART
naive
bayes
classiﬁer
ICA

Program
m
ing
&
Scripting
Skills
M
athem
atics
&
Statistics
Domain Expertise
DT
Analytics
Traditional
Research
DangerZone!
~statisticulation
ML
[2] http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
[1] Statisticulation “How to Lie with Statistics” Darrell Huff
[3] https://portal.data-tactics-corp.com/sites/analytics/Wiki/AnalyticsFAQ.aspx

Why Analytics [Business]???
Why are analytics important?
(Business, Analytics, Practical)
"We need to stop reinventing the cloud
and start using it!"
(Dave Boyd)

Analytics:
No Free Lunch (NFL) theorems: no algorithm performs better
than any other when their performance is averaged uniformly
over all possible problems of a particular type. Algorithms must
be designed for a particular domain or style of problem, and that
there is no such thing as a general purpose algorithm.
Why Analytics [Analytics]???

Marty doesn’t scale - none of us do.
Data Scales
Web Scales
Academic Publications Scale
IC Scales
N
t
t
Why Analytics [Practical]???

Why Analytics [Practical]???
“…the alternative to good statistics is not “no
statistics,” it’s bad statistics. People who argue
against statistical reasoning often end up backing up
their arguments with whatever numbers they have at
their command, over- or under-adjusting in their
eagerness to avoid anything systematic” Bill James

"companies that have massive amounts of data
without massive amounts of clue are going to be
displaced by startups that have less data but more
clue" (Tim O’Reilly)
Philosophy:

Philosophy:
We are NOT “Data Agnostic”
...this should represent an early warning
system about our culture. The IT notion
of data is dead.

Analytics in Perspective...
http://datatactics.blogspot.com/2013/07/analytics-in-perspective-inquiry-into.html
Analytics in Perspective: An Inquiry into Modes of Inquiry

“Analytics in Perspective” reﬂects how people arrive at
decisions.
GOOD: Induction, Abduction, Circumscription, Counterfactuals.
BAD: Deduction, Speculation, Justiﬁcation, Groupthink
Analytics in Perspective...

Identifying Smugglers
Leveraging Big Spatio-Temporal Data

Background: The Strait of Hormuz
Importance:
• Oil
• Embargo
• Smuggling

How to Catch Smugglers
In order to stop smugglers, we must identify:
1. Which boats are undertaking illicit activities
2. Where illicit activities are taking place
3. Points of departure/arrival of suspicious ships

A Diﬃcult Task: Too Much Data
AIS (transponder) provides ship-level data:
• Ship location (lat-long)
• Ship speed
• Ship bearing
• Ship “purpose”
• Time stamp
About 0.5M pings from 1,300 boats between
March 2012 and January 2013.

A Diﬃcult Task: Too Much Data

A Diﬃcult Task: Too Little Data
Individual pings or tracks not useful: no point of
comparison
Similarly, small duration plots are too thin to provide
analytic leverage.

.
A single boat:

.
A single day:

A Diﬃcult Task: Many Types of Boats

Solution: Analytics
Use a statistical model to discover patterns in
the data…
…then identify observations (boat-times) that do
not ﬁt those patterns.
Goal: Identify boats, place, and times that exhibit
or house discrepant behavior.

Characteristics of a Good Model
A good model for this data should:
• Leverage all of the available data
• Take advantage of local information (not global patterns)
• Be able to accommodate a variety of patterns (shipping,
ﬁshing, etc)
• Be able to identify ships that are only occasionally deviant
• Identify place-times where deviant activity occurs
• Be estimable with reasonable computational resources

The Model
A local, unsupervised-as-supervised learning,
bagged, probability model.
A LUBaP model?

The Model
We want to compare apples-to-apples; that is,
treat nearby (spatio-temporally) boats the same,
don't compare them to far-ﬂung ones.
Assign each observation to a geographically
constrained grid square.

The Model

The Model
Let m denote the number of observations in a particular grid
square. Then, in each square, add m additional observations
with the following characteristics:
•position, drawn from bivariate uniform distribution
•speed, drawn with replacement from empirical distribution
•time of observation, drawn from a uniform distribution
Now, the task is no longer unsupervised, but supervised.
->Model the probability of a boat being a ``real'' boat.

The Model

The Model
•Turned outlier detection, a poorly structured problem, into
modeling a binary target, a very well-understood problem
•Now, simply model the probability that each boat is “real”
•Apply logistic regression to each grid square
•Allow the ﬂexibility (order) of the model ﬁt (splines,
interactions) to depend on the data density in each square
(more data, richer model).
•logit(“real”) = f(speed, location, time)

The Model
Problem: Predictions may be arbitrary due to
random assignment and grid coarseness.

The Model
Problem: Predictions may be arbitrary due to
random assignment and grid coarseness.
Solution:
1. Create multiple grids with diﬀerent positions.
2. Re-run the local model in each square, for
each diﬀerent grid.
3. Aggregate the predicted probabilities for each
observation, in each grid, by averaging.

Computational Eﬃciency
Estimating a ﬂexible model in each of ~300 grid squares, for
each of 6 grids, means estimating ~1,800 logistic models!
Not a problem, because:
• each one has limited amounts of data (most algorithms take
exponentially longer as a function of data size)
• each local model is separate, allowing for parallel
processing
Computation on my laptop takes ~4 minutes after simple
parallelization across cores.

What is the Output from this Model?
•Predicted probability of each boat-time (i.e. observation)
being a real boat.
•High probabilities indicate observations doing something
“normal” or “predictable.”
•Low probabilities indicate observations doing something
“discrepant.”
Ship ID Lat Long Speed Timestamp Pr
623432 24.546 55.005 9.8 1203221230 0.78
874627 24.716 55.108 12.4 1209242230 0.08
523881 25.128 54.807 4.2 1206120947 0.64

Value I: Location of Illicit Activities

Value II: Identify Devious Boats

Value III: Prioritized List of Suspect Boats
•Model generates probabilities on an interval scale
•Facilitates eﬃcient use of scarce enforcement resources

Lessons Learned
Analytics is a powerful tool for identifying patterns in big data.
Identifying outliers is predicated on identifying patterns.
LUBaP models are a powerful tool for outlier detection.
This model utilizes no subject matter expertise and a simple
probability model (implications: portable across domains; fast)

What’s the Next Hot Thing?
Unsupervised Scaling of Text Data

Analyzing Text is Important
The preponderance of data created today is free text, not
structured numerical data.
One thing people want to do with text is “scale” it; that is, rank
order it according to an underlying continuum.
Examples:
-put a numerical value on what each product reviewer thinks of
a particular product
-generate a measure of the extremism of Iranian clerics based
on their writings

Analyzing Text is Diﬃcult
Text data is unstructured, and messy.
“I thought I would love the iPhone, but it’s actually not that
great.”
Standard approaches:
1. Dictionary: Create a numeric value for many content-laden
words; compare texts to the dictionary.
2. Estimation: Hand-score many texts; use the scores as a
basis for training a statistical model for other texts.

A New Approach
Each author’s use of a word implies they “support” that
word, as opposed to words they don’t use. The
model, developed for scaling ideological positions of
legislators from votes, can be applied to word use.
Beneﬁts:
1: No dictionary!
2: Language invariant!
https://github.com/DataTacticsCorp/text-analysis

Preliminary Example
Pulled down 2000 tweets, 1000 each with the hashtags #prolife
and #prochoice.
Drop the hashtags (no cheating!), pre-process the text data, and
run the model.

Output

Local Events, Worldwide Impact

Localview
Localview also known as “Lv”, is a Cloud/Web
based proprietary Dashboard with an
advanced analytics framework – the desired
end state is an integrated data mining,
knowledge discovery and pattern recognition
of social and spatial pattering. Lv will provide
end-users with globally and locally available
historical information as well as globally and
locally available real-time social media data
feed. This service includes; news, on the spot
statistics using a proprietary Data Tactics Tool
called
©
“ZoomStat”, historical facts, social media, economics, security, military,
infrastructure, health, aid, natural disasters, war, entertainment, weather,
transportation, and travel. All results will be analyzed, ingested,
normalized, and then plotted on a dynamic and interactive global map.

...by the numbers
 7 volunteered & part time team members (NO OVERHEAD)
 ﬁrst DEMO delivered in 86 days
 832 hours of research & development time

The Team:
The Team
backend development frontend development data analysis development
Marty A
Joe A
Joon K
Annie W Dave P
Rich H
Shenoa H

Evolution:

Development Process
Lv Development Process

End-Users:
Law Enforcement
IC & DoD Commercial

Directional Space Time
Analytics
Base-Rate Fallacy

Directional Space Time Analytics
Data Tactics has been working on a set of problems that
require considered solutions. The following method
compares distributions at two points in time, with a
particular focus on changes in the overall morphology of the
distribution as well as mobility of individual observations
within the distribution over that same period of time and
contextually accounting for neighborhood eﬀects. These
dynamics are illuminating and communicate time and
explicitly account for underlying spatial dimension (Wy).
Based on the integration of a dynamic local space-time
together with direction statistics these methods provide
insights on the role of spatial dependence and uncontrolled
variance over time and space.

This analysis demonstrates the utility of directional space time analytics
on regional stability distribution dynamics. Drawing on recent advances
in geovisualization [1], we suggest a spatially explicit view of mobility.
Based on the integration of a dynamic local indicator of spatial
association together with directional statistics and mapped data points
to each observation, this framework provides new insights on the role of
spatial dependence in regional stability and change.
These approaches have been illustrated with state level incomes in the
U.S. (1969-2008), Gross Domestic Product (1960 - 2011) Failed State
Index (2010 - 2012), and GMTI data (t0, t1).
[1] Murray, A. T., Liu, Y., Rey, S. J., and Anselin, L. (2010). Exploring movement object patterns.

Per Capita Gross Domestic Product
A measure of the total output of a country that takes the gross domestic product (GDP)
and divides it by the number of people in the country. The per capita GDP is especially
useful when comparing one country to another because it shows the relative
performance of the countries. A rise in per capita GDP signals growth in the economy
and tends to translate as an increase in productivity.
GDP is widely used by economists to gauge economic recession and recovery and an
economy's general monetary ability to address externalities. It is not meant to measure
externalities. It serves as a general metric for a nominal monetary standard of living and
is not adjusted for costs of living within a region.
Gross Domestic Product
GDP = private consumption + gross investment + government spending + (exports − imports), or

GDP per. Capita
Time Span: 1960 to 2011 (51 temporal bin(s), 1 year intervals): 2000 to 2011 (12 temporal
bin(s), 1 year intervals);
Spatial Area: Global;
Original Sample: 202 obs;
Data processing: imputation;
Pruned Sample: 145 observations;
Method: Directional Local Indicator of Spatial Autocorrelation (Moran’s I) with space-time
classiﬁcations of High-high (Hh), high-High, Low-Low (LL), High Low (HL), Low-High (LH);
Spatial Weights: knn4;

> describe(dlisa$yr2000)
> describe(dlisa$yr2011)
V. Name n mean sd median mad min max range skew kurtosis
yr2000 145 5759 9534 1491 1831 87 46453 46366 2.12 3.72
yr2011 145 13292 20621 4666 5841 231 114232 114001 2.46 6.54

https://vimeo.com/69775085

2000:2011 (12 temporal bin(s), 1 year intervals);

What is wrong with Vermont[1]?
- Seemingly nothing!
- Lies within head of approximately normal distribution
- Not an outlier in a classical statistical sense
- Vermont remains below the US average but is
closing the gap.
[1] State Median Income

State Median Income
Time Span: 1969 to 2008 (40 temporal bin(s), 1 year intervals)
Spatial Area: Contiguous United States;
Original Sample: 48 obs;
Method: Directional Local Indicator of Spatial Autocorrelation (Moran’s I) with space-time
classiﬁcations of High-high (Hh), high-High, Low-Low (LL), High Low (HL), Low-High (LH);
Spatial Weights: Rook Contiguity;

1969:2008 (40 temporal bin(s), 1 year intervals)

Core Values:
Localview as an ecosystem:
Most existing big data analyses of social media are confined to a
single platform. However, most of the topics of interest to such
studies, such as influence or information flow can rarely be confined
to the Internet, let alone to a single platform. Understandable
difficulty in obtaining high-quality multi-platform data does not mean
that we can treat a single platform as a closed and insular system,
as if human information flows were all gases in a chamber.
“Shapes of stories into computers...” Kurt Vonnegut
Nate Silver - Cognition2
; Small Multiples; Tukey vs. Tufte
http://kottke.org/11/09/kurt-vonnegut-explains-the-shapes-of-stories

Core Values:
Open-source software where possible.
-Bigger data means bigger cost.
-Scientific Python and R Computing Language reached maturity years ago.
Data = Rough + Smooth Qualities
Rough = impulsive, spiky signal: outliers; Smooth = pervasive
Leverage analytics to help understand patterns in data as well as outliers - so called rough
and smooth elements of data. The “smooth” and the “rough” patterns in data are
informative, depending on the specific questions customers have.
Local, as opposed to global or whole-map statistics:
We believe that micro-level, local patterns are often of key interest, and can be
obscured or distorted by attempts to fit global models to local data.
Analytical Pluralism:
Mutli-method approaches dominate single-method approaches. Rather than craft a single
statistical model to answer a customer question, we attack problems from several angles
simultaneously, deriving insights from areas of overlap and divergence in the pattern of findings.
Methodological pathways:
Blend nomothetic and idiographic approaches.

Core Values:

Analytical Resources:
https://portal.data-tactics-corp.com/sites/analytics/SitePages/Home.aspx

https://github.com/DataTacticsCorp

http://datatactics.blogspot.com

...on the horizon.
...On the Horizon:
DT & USMA Department of Systems Engineering partner together and leverage
the Advanced Individual Academic Development Program.
Rstudio: analytics.data-tactics-corp.com; PostgreSQL: analytics.data-tactics-corp.com Port: 5432
https://github.com/rheimann/kiva-master

Data Tactics & US Military Academy:
A Prime in Microfinance using KIVA
Rstudio: analytics.data-tactics-corp.com; PostgreSQL: analytics.data-tactics-corp.com Port: 5432
Understanding the complex nature of microfinance more completely:
The US military is directly involved in microfinance (Iraq & Afghanistan), working primarily
through Provincial Reconstruction Teams (PRTs). Funded by the DoD and DoS; the
operational requirements of these agencies create a need to demonstrate quick impact on
economic recovery and therefore the goal is to report high numbers of loans.
Technical complexities separate this data from other datasets:
Heterogeneous forms: structured/unstructured/nominal,ordinal, quantitative/temporal/
geographic/multi-lingual/multiple relationships(lenders to recipients) - multiple sectors/
missing data. Data cleansing is hard!
Big Data(ish): $420M (USD), 1.1 million lenders, 580,000 loans, 250 partners, 4.1M
transactions, 3 WHOLE GBs. (https://vimeo.com/28413747)
Broad appeal:
...government to defense to finance to banking to non-profit organizations to THE POOR.
https://github.com/rheimann/kiva-master

...on the horizon.
...On the Horizon:
DT & The Institute for the Study of War will collaborate in a balanced but largely
quantitative approach to analyzing revolutions and the role social media plays with
particular focus on the Iraq Spring.

...on the horizon.
...on the Horizon:
Data Science for Program Managers (late September / early October)
Analytics Brown Bag Volume II (October / Early November)

Thank you...
83
Questions?
Homepage: http://www.data-tactics.com
Blog: http://datatactics.blogspot.com
Twitter: https://twitter.com/DataTactics
Or, me (Rich Heimann) at rheimann@data-tactics-corp.com

Data Tactics Analytics Brown Bag (Aug 22, 2013)

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