Past Meetup

Identifying Smugglers: Local Outlier Detection in Big Geospatial Data

This Meetup is past

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Details

For our November Meetup, we're thrilled to bring you a presentation about a key technique used when analyzing geospatial data -- detection of outliers. As sensor data gets cheaper and more ubiquitous, as business data becomes precisely geotagged, and as locality becomes key in everything from surveys to log files, the well-rounded data scientist needs to be familiar with techniques for effectively working with latitudes, longitudes, points, and geometric objects. Nathan Danneman will talk about techniques he's used for finding geographic outliers -- points that may be signal in the noise, or perhaps noise in the signal -- when you don't have a good model of the data-generating process.

Agenda:

6:30pm -- Networking, Empenadas, and Refreshments

7:00pm -- Introduction

7:15pm -- Presentations and discussion

8:30pm -- Adjourn for Data Drinks (Circa, 2221 I St.)

Abstract:

This talk describes a method for unsupervised, local outlier detection that does not rely on specifying a parametric model for the unlabelled data. The method is a unique amalgam of several “off-the-shelf” techniques, and creates a potent, flexible, scalable solution for identifying local (Type II) outliers. I apply this model to transponder data from ships in the Strait of Hormuz to demonstrate its capabilities, as well as some of the challenges associated with its use.

Bio:

Nathan Danneman (http://www.nathandanneman.com/) is an analytics engineer at Data Tactics (http://www.data-tactics.com/), where he analyzes geospatial, textual, and cyber-related data. He holds a PhD in political science from Emory University (2013), with focus areas in applied statistics and international conflict. Some of his past and current work includes quantitative studies of human rights abuses, formal and quantitative modeling of international conflict mediation, and a book on mining and analyzing social media data.

Sponsors:

This event is sponsored by Intridea (http://www.intridea.com/), Statistics.com (http://bit.ly/12YljkP), Elder Research (http://datamininglab.com/), MemSQL (http://memsql.com), and ParkMe.

Parking:

For those driving, we encourage you to find parking for this event via our sponsor, ParkMe (http://www.parkme.com/). ParkMe will help you find the closest, cheapest parking, and has iPhone (https://itunes.apple.com/us/app/parkme-parking-find-cheapest/id417605484?mt=8) and Android (https://play.google.com/store/apps/details?id=com.parkme.consumer) apps. Click here (http://www.parkme.com/map/?lat=38.90067396998184&lng=-77.04746074998474&zoom=17) for their map of nearby parking for this event.