In this webinar you learn some of the techniques and resources that can help you bolster your Spatial Data Science skills. You can watch the recorded webinar at: https://go.carto.com/webinars/spatial-expert-recorded

1. How to Become a Spatial Data
Scientist in 2020
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2. CARTO — Unlock the power of spatial analysis
Introductions
Giulia Carella
Data Scientist
at CARTO
Steve Isaac
Content Marketing Manager
at CARTO

3. CARTO — Unlock the power of spatial analysis
Only 1 in 3 Data Scientists
have significant expertise in
spatial techniques

4. CARTO — Unlock the power of spatial analysis
What percentage of your Data Science
team has significant experience in Spatial?

5. CARTO — Unlock the power of spatial analysis
https://go.carto.com/ebooks/spatial-data-science
Ready to become
a Spatial Expert?

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Ebook
Overview

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https://github.com/CartoDB/data-science-book
Notebooks

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Power Data Science models with location data and spatial analysis

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Chapter 1
What is Spatial
Data Science and
Why is it
Important?
“Spatial data science
treats location,
distance, and
spatial interaction as
core aspects of the
data”
(Luc Anselin)

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Spatial data comes in all forms and shapes
Chapter 1

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Spatial dependence
“Everything is related to everything else, but near things are more related than distant things.” (Tobler, 1970)
● CONTINUOUS PROCESSES
Gaussian Processes (GP),
covariance functions, and variograms
Chapter 1

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● DISCRETE PROCESSES
neighborhood structures and
autocorrelation statistics (e.g. Moran’s I)
Spatial dependence
“Everything is related to everything else, but near things are more related than distant things.” (Tobler, 1970)
Chapter 1

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● POINT PATTERNS
Complete Spatial Randomness
(e.g. summary statistics, G-function)
Spatial dependence
“Everything is related to everything else, but near things are more related than distant things.” (Tobler, 1970)
Chapter 1

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Chapter 2
Spatial Modeling
Leveraging Location
in Prediction

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Spatial modelling
The mean structure
e.g. a function of some
covariates
The residual (or what is not
explained by the mean structure)
What we are trying to
model (or the response
variable)
Chapter 2

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● Continuous Spatial Error Models (GP)
● Discrete Spatial Error Models (Gaussian Markov Random
Fields, GMRF)
● Spatially Varying Coefficient Models
● Spatio-temporal models
● Spatial confounding
● Validation tools
Spatial modelling
Chapter 2

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Continuous Spatial Error Models Discrete Spatial Error Models
Spatial modelling
Chapter 2

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Continuous Spatial Error Models Discrete Spatial Error Models
Spatial modelling
Chapter 2

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Chapter 3
Spatial Clustering
and Regionalization

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Uses data attributes to create classes that,
via those attributes, are different while
staying alike within that category
● Longitude and latitude can be included as
one of these attributes
● e.g. K-means
Clustering Spatial Clustering
Groups together points that are close to each
other based on a distance measurement
● e.g. DBSCAN, GENERALIZED DBSCAN
Clustering VS Spatial Clustering
Chapter 3

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Clustering Regionalization
Using SKATERUsing DBSCAN
Clustering VS Regionalization
Chapter 3

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Chapter 4
Logistics
Optimization with
Spatial Analysis

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A typical optimization model consists of the following components:
● Decision Variables
e.g. whether to open a distribution center (DC) at a specific location, whether a zip code is
served by a DC, or which truck will serve one customer and when)
● Objective Function
e.g. costs, service level, etc.
● Constraints
e.g. physical constraints (a truck cannot transport more than its capacity), business
constraints (every client should not be further than 20 miles away from the closest DC)
Optimization
Chapter 4

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Exact VS approximate algorithms
Find the actual optimal solution
● e.g. Simplex Algorithm
● Google OR-Tools
Exact Approximate
Close as possible to the optimum value in a
reasonable amount of time
● e.g. Simulated Annealing, Tabu Search
● Google OR-Tools, Python packages (e.g.
simanneal)
Chapter 4

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Solving the Traveling Salesman Problem
Given a list of cities and the distances between each pair of cities,
what is the shortest possible route that visits each city and returns to the origin city?
Christofides Algorithm Ant Optimization
Chapter 4

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It’s time for a real world example!

28. Thanks for listening!
Any questions?
Request a demo at CARTO.COM
Steve Isaac
Content Marketing Manager // sisaac@carto.com
Giulia Carella
Data Scientist // giulia@carto.com