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

USMADARPA%...%

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)...Thursday, August 22, 13

Data Tactics Analytics Practice 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 1stCharles 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

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Data Tactics Analytics Practice

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Skills Mathematics & Statistics

Domain Expertise

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[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


https://s3.amazonaws.com/aws.drewconway.com/viz/venn_diagram/data_science.html

https://s3.amazonaws.com/aws.drewconway.com/viz/venn_diagram/data_science.html

https://portal.data-tactics-corp.com/sites/analytics/Wiki/AnalyticsFAQ.aspx

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)


Why are analytics important? (Business, Analytics, Practical)

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

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Why Analytics [Practical]???


Why Analytics [Practical]??? Why are analytics important?

(Business, 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 activities2. Where illicit activities are taking place3. 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 Difficult Task: Too Little Data.

A single boat:


A Difficult Task: Too Little Data.

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

A local, unsupervised-as-supervised learning, bagged, probability 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

A local, unsupervised-as-supervised learning, bagged, probability 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 AJoon K

Annie W Dave PRich H

Shenoa H


Evolution:


Development Process Lv Development Process


End-Users:

Law Enforcement

IC & DoD Commercial


Directional Space Time Analytics

Base-Rate Fallacy



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


https://en.wikipedia.org/wiki/Consumption_(economics)

https://en.wikipedia.org/wiki/Consumption_(economics)

https://en.wikipedia.org/wiki/Gross_private_domestic_investment

https://en.wikipedia.org/wiki/Gross_private_domestic_investment

https://en.wikipedia.org/wiki/Government_spending

https://en.wikipedia.org/wiki/Government_spending

https://en.wikipedia.org/wiki/Export

https://en.wikipedia.org/wiki/Export

https://en.wikipedia.org/wiki/Import

https://en.wikipedia.org/wiki/Import

GDP per. CapitaTime 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 3.72yr2011 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 IncomeTime 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 VonnegutNate Silver - Cognition2; Small Multiples; Tukey vs. Tufte

http://kottke.org/11/09/kurt-vonnegut-explains-the-shapes-of-storiesThursday, August 22, 13

http://kottke.org/11/09/kurt-vonnegut-explains-the-shapes-of-stories

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 QualitiesRough = 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


http://analytics.data-tactics-corp.com/






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.











...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.comTwitter: https://twitter.com/DataTactics

Or, me (Rich Heimann) at [email protected]


http://www.data-tactics.com






mailto:[email protected]

mailto:[email protected]

Data Tactics Analytics Brown Bag (Aug 22, 2013)

Education

Transcript of Data Tactics Analytics Brown Bag (Aug 22, 2013)