Sample of talks, workshops, and webinars I gave when conferences were normal.

Unlock the Power of Spatial Analysis using CARTO and Python

CARTO Webinar — Internet 2019-09-25

Learn how to integrate spatial data and analysis to your data science models using CARTOframes—a Python package that allows data scientists to seamlessly integrate CARTO maps, data, and analysis into their current environment.

We have recently launched a new CARTOframes version packed with new functionality to build powerful end-to-end spatial data science workflows. 

In this webinar, Andy Eschbacher (Data Scientist) and César Monteserín (Product Manager) show how you can power your spatial data science journey with CARTOframes:

• Creating interactive visualizations straight out of Jupyter notebooks
• Enriching your data with best-in-class data streams
• Analyzing your data and getting insights using your own libraries, functions, and workflows
• Uploading and sharing your maps

We go over CARTOframes new functionality and real-life examples. 

A pdf of the slides of the presentation is also available to download here. 

Adding Spatial Structure and Data to Machine Learning Models

Data Council — San Francisco, CA, USA 2019-04-17

Spatial data science uses many of the same techniques and algorithm as traditional data science, but the spatial component can add a large amount of additional information by combining with other sources at the same location (e.g., census, geolocated tweets), using realtime routing services, or using the spatial structure of the distribution of the data.
In this talk, I will highlight work we have done with constraint-based clustering, probabilistic principal component analysis, and vanilla random forest that take special advantage of the spatial part of the data. I will further show how we build models from a variety of sources, including mobility data, points of interest, and accurate routing data.
For example, using the Python package CVXOPT, we solved a linear optimization problem that optimally distributes an asset from a source to a drain according to the road network and constraints that the drains cannot be over capacity, occasionally have fixed assignments, and all the asset has to be moved.

Maps & Machine Learning: Best Practices for Great Cartography in Data Science

ODSC East — Boston, MA, USA 2019-05-02

Making good maps in data science is hard. Maps are another form of data visualization with its own set of design principles to bring out the story in the data. Data scientists need for maps varies, but a very common use case is to quickly generate a data visualization iteratively to better understand model outputs, spatial variations in the data, and more.
In this mini-tutorial, I will cover best practices for data scientists needing maps -- ideas pulled from cartography and honed over the years working in the intersection of open source GIS and data science. Using open source tools, I will show how to make patterns more apparent, how to build interactive maps so that it is easier to explore data on larger and smaller scales, and list some cartographic tips and tricks. The goal is that data viz with maps will be made easier so that you can think about your data science problems instead of worrying about visualizing data on a map.

Best Practices for Spatial Data Science using CARTO and Python

CARTO webinar — Internet 2019-01-10

Learn how you can combine CARTO and Python for spatial data science from the comfort of your own Jupyter notebook. In this technical webinar, Andy Eschbacher (Senior Data Scientist at CARTO) and Joe Pringle (VP - North America at CARTO) will show how to apply CARTOframes and CARTO's Python SDK to build powerful end-to-end spatial data science workflows.

Blackbelt in spatial analytics

CARTO Locations — Madrid, Spain 2018-04-20

Spatial Data Science Analyses

ODSC East — Boston, Mass, USA 2018-05-01

Spatial data science uses many of the same techniques and algorithm as traditional data science, but the spatial component can add a large amount of additional information by combining with other sources at the same location (e.g., census, geolocated tweets), using realtime routing services, or using the spatial structure of the distribution of the data.

In this talk, I will highlight work we have done in linear optimization, genetic algorithms, and constraint-based clustering that take specital advantage of the spatial part of the data. For example, using the Python package CVXOPT, we solved a linear optimization problem that optimally distributes an asset from a source to a drain according to the road network and constraints that the drains cannot be over capacity, occasionally have fixed assignments, and all the asset has to be moved.

Open Spatial Data Science

ODSC Europe — London, England 2017-10-14

The spatial part of a dataset gives more than just a lat/long. It allows you to thread a needle through any other spatial dataset that exists at that location: census, GPS tracks, data from a municipality's open data portal, and so much more. The spatial part is a key to a multidimensional world.

Augmenting your spatial data is only one piece, though. Once you know a location, you can use the measurements at the locations around you by appealing to Tobler's First Law of geography: "Everything is related to everything else, but near things are more related than distance things." Using this, statistics of geography allow you to find spatial correlations (Moran's I), calculate spatial regression (geographically weighted regression), and uncover spatial outliers (Getis-Ord's G*). At CARTO we're building these powerful techniques into an API (https://github.com/CartoDB/crankshaft) where data scientists can extract more value from his/her spatial data. Combined with the data augmentation process that we call the Data Observatory (https://carto.com/data-observatory), data scientists are freer and more enabled to explore their data in the context of the world.

Teaching New Cartography, with Rich Donohue

Nacis Annual Meeting — Colorado Springs, CO 2016-10-20

Demo CARTO's Data Observatory that I helped build

Hacks/Hackers NYC — New York City, NY 2016-09-06

Building a map thinking machine: Demo One-click Mapping algorithm that I wrote

Hacks/Hackers NYC — New York City, NY 2015-08-11

Taking PostgreSQL and Analytics to the Next Level with Python

PGConf US — Brooklyn, NY 2016-04-18

Work using PL/Python to leverage the Python ecosystem with analysis that uses data stored in PostgreSQL

How the L Train Closure Affected Open Source Development of CartoDB

NY Open Statistical Programming Meetup — New York, NY 2016-07-11

Discussing the work my team did on the Looking at the L project.

Temporal Maps leading to new views in Spatial Analysis

FOSS4G — Seoul, South Korea 2015-09-17

Everyone's a Geographer

FOSS4G US — San Francisco, CA 2015-03-10

Discussing my work developing open source geospatial curriculum for CARTO's Map Academy

Update on CARTO's Spatial Analytics Extension crankshaft

FOSS4G — Boston, MA 2017-08-18

Discussion of the current state of crankshaft, CARTO's spatial analytic extension to PostgreSQL

Programmatic cartography in cartoframes

Nacis Annual Meeting — Montréal, Canada 2017-10-11

Discussion of the cartographic techniques built into cartoframes to help data scientist create better cartographic outputs based on the type of data they are visualizing

Teaching in an Open Source World

Texas GIS Forum — Austin, Texas 2015-10-22

Presentation of my philosophy and work on education in the classroom as a physics educator and then at a startup that uses web mapping technologies

The Canarsie Closure and Carto: How a news leak altered the course of Carto's Development

NYC Salon — New York, New York 2016-07-10

Discussion about my team's work on the L Train closure. Talk started at 8th Ave L train and we looked at damage to the tunnel along the way. Once in Bushwick, I presented more in depth about our work and participants designed their own mitigation plan for what will happen for the 18 months the closure will be in effect.

Maps for Telling News Stories

Bar Camp Philly — Philadelphia, PA 2015-05-08

I share some of my favorite recent maps used for contextualizing the news.