This document discusses the rise of geospatial data science and the differences between geospatial data science and traditional GIS. It notes that everyday 2.5 quintillion bytes of data are generated, 80% of which has a geospatial component. Geospatial data science bridges data science and GIS by treating location as core to the data. This field has grown due to increasing data availability, geospatial libraries, cloud computing, and the need to combine GIS and data science skills. The future of geospatial data science involves increased AI/ML use, more industry applications, and significant market and job growth.

1. The Geography of
Geospatial
An Exploration of Geospatial
Data Science and Location

2. Everyday 2.5
Quintillion Bytes of
Data are Generated

3. 80%
Of all Data has a Geospatial
Component

4. 90+%
Is the Percentage of Data
that most Geospatial
Professionals believe
contains Geospatial Data
or is Location Aware

6. What is Geospatial Data Science
● Geospatial Data Science is Nested Between Pure GIS and Pure Data Science
● Geospatial Data Science Treats Location and Geospatial Interaction as the
Core Aspect of the Data. Data Through a Geospatial Lense
● Touches Many Verticals
○ Government
○ Management Consulting
○ Real Estate
○ Retail
○ Utilities and Telecommunications
○ And Many More

7. What is the Difference Between Geospatial Data Science
and GIS
● After 20+ years GIS is still considered a ‘niche’ field
● GIS Professionals tend to be siloed off from other business units
○ “Go ask those map people to make a map”
● GIS tends to be synonymous with the technology more so than the analysis
○ GIS Server
○ GIS Database
○ Websites
● Specialized Data Types for GIS software (.shp, .e00, .dwg)
● Coding Languages Specific to Proprietary Software Vendors
● Tend to be more Map focused than Data focused (although this is starting to
change)

8. Rise of the Geospatial Data Scientist- Technincal Reasons
● Increasing Availability and Access to Geospatial Data
● Increasing Number of Geospatial Libraries in Python and R
○ R - 185
○ Python - over 700
● Robust Spatial Analysis in Common Databases
● Geospatial Analysis Began to Become More Common
● Increase in Access to Cloud (Storage and Compute) Increased Speed of
Geospatial Analytics
● Big Data Sets from IoT and other Devices with Location Data

9. Rise of the Geospatial Data Scientist- Business Reasons
● Most Organizations Siloed GIS Teams and Data Science Teams
○ Territorial Conflict
○ Mistrust Between Business Units
● On the Data Science Teams 33% of the Data Scientists had “Expertise” in
Geospatial Analysis
● Members of the GIS Team tended to not have Data Science or Spatial
Statistics “Expertise”
● Geospatial Data and Processing was Costly, but with Open Source Software,
Cloud Computing and Inexpensive Access to Data

10. Rise of the Geospatial Data Scientist- Business Reasons
Organizations with Data Science/GIS Teams

11. Rise of the Geospatial Data Scientist- Business Reasons
Frequency of Collaboration Between Data Science and GIS Teams

12. Geospatial Data Scientists Evolved To
● Bridge the Gaps Between Silo’d Business Units
● Bring Statistics and Spatial Skill to Analytics
● Work with both Data Scientists and GIS Teams to Develop a Coherent Spatial
Data Strategy

13. Business Value for Geospatial Data
● Maps are an Engaging Form of Data Visualization
● Spatial Foresight
● Identification of Investment Risks
● “Ground Truth”
● Location Impact Supply Chain Logistics

14. Maps for Visualizing Data

15. Spatial Foresight

16. Identification of Investment Risks

17. Ground Truth

18. Location’s Impact On Supply Chain Logistics

19. Where are the Geospatial Data Scientist Jobs
Mature Job Markets
● National Capital Region (DC, Baltimore, Annapolis, Richmond)
○ Government
○ Defense/Intelligence
○ Non Profit
○ Environmental
○ Start Ups
○ Business Management
● Houston, TX
○ Oil and Gas
● Denver/Colorado Springs
○ Government
○ Defense/Intelligence
○ Oil and Gas
○ Business Management

20. Where are the Geospatial Data Scientist Jobs
Developing Job Markets
● NYC Area
○ Government
○ Finance/Investments
○ Start-Ups
○ Business Management
● Boston
○ Insurance
○ Start-Ups
○ Government
● San Francisco
○ Software Development
○ Start-Ups

21. Where are the Geospatial Data Scientist Jobs
Developing Job Markets
● Chicago
○ Finance/Investments
○ Start-Ups
○ Business Management
● St Louis
○ Defense/Intelligence
○ Precision Agriculture
● Seattle
○ Software Development
○ Start Ups
○ Government

22. What does the Future Look like for Geospatial Data Scientist
● Increasing use of ML/DL and AI for Geospatial Analytics
● Exponential Growth in Data (EO, IoT and Sensor Data)
● Expanding into Different Industries and Verticals
● Over the next 2 Years 68% of Firms will Increase Their Investment in
Geospatial Data and Geospatial Data Scientist*
● Geospatial Data Market will increase for 38.65b (2017) to 174.65 Billion (2027)*
*Geospatial Investment and Job Market are Expected to feel little impact COVID-19

23. Any Questions

24. Thank You for Your Time and Attention
Twitter: @Spatial_Punk
Email: Todd.Barr@gmail.com