The current pandemic is a global crisis that impacts every government – Federal, National or Local – in every country, but that also requires adapted local solutions according to demographics, behaviors, geography or economics. Relying on consistent geolocated data on territories about the situation, needs, resources, material, people… is key for all persons involved in this fight. Location Analytics can help Governments and Public Sector Authorities involved in Healthcare, Law enforcement, Transport, Economics to better understand the situation and its evolution, to make the right decisions and improve execution. Discover in these slides: - How you can detect and monitor areas at risk, by correlating business and contextual data - How you can identify hidden patterns, by using spatial time-series - How you can segment and identify the most vulnerable populations, by using spatial filters such as distance or isochrony - How you can offer the best services to every citizens, by leveraging socio-economical indicators alongside your business data

1. How Location Data & Analytics Help
Governments and the Public Sector
Fight Against Coronavirus impacts
Decision-Making in Times of Covid
2020, © All rights reserved.
WEBINAR

2. Agenda
A Story About Decisions in Times of Covid
I. Mutual introductions
II. Decision-making in critical times
III. Location Analytics to the rescue
IV. Demonstration on operational proximity
V. How far can Location Analytics decision-making go?
VI. Available solutions to get you going
VII. Q&A
2020, © All rights reserved.

3. You and your peers today
BI Analysts,
DS,
Consultants
33%
Managers
42%
Developers
25%
Local Gov.
or Agency
50%
Central
Gov.
25%
Private
25%
BI
Platform
64%
SQL
Database
18%
Other
18%
Responsibility Organization Data platforms
2020, © All rights reserved.

4. Countries represented today
by attendees

5. Your Galigeo Hosts Today
2020, © All rights reserved.
Christian Tapia
Business Development
Vincent Dechandon
Product Manager

6. Retail
Healthcare
Public Sector
Government
Transport
IndustryPolice& Security
Telcos
Energy
Finance
April 2020
Location Analytics Legitimacy Since 2001

7. Retail
Healthcare
Public Sector
Government
Transport
IndustryPolice& Security
Telcos
Energy
Finance
April 2020
Location Analytics Legitimacy Since 2001
Forrester Wave™ report 2018, Galigeo
Among top 8 Location Intelligence Platforms

8. Planet, we have a serious problem…
2020, © All rights reserved.

9. Information for decision-making
•How is Covid going to affect my loved ones, me,
my community, my country?
•The 5 Ws of information
•Who, What, Where, When and Why
•Knowing where is essential
2020, © All rights reserved.

10. Location Analytics Blends the 5 Ws to Solve Problems
2020, © All rights reserved.
Location Data Becomes Practicable to Deliver Novel Insights
Business Data
Contextual Data
(socio-economical,
weather, trafic, pandemic, …)
Mobility Data
Blending
Location
Analytics

11. Location Analytics Extends Decision-Making
2020, © All rights reserved.
3) Operations
1) Visualization
2) Analytics
Spatial Selection
Choropleth, custom themes
Zoom/unzoom
Bi-directionality
Drilldown
Heatmaps
HierarchiesTime series
Clusters
Flowmaps
Drive-time
Collaboration
Data import
Annotations
Itineraries
Geocoding

12. Data, Location & Analytics
To Find Answers To Your Questions
A. I need to help answer location related pandemic questions (e.g.
deconfinement management)
B. I need to map my own ratios/measures and compare to
government data
C. I need to provide operational proximity data to field forces or
decisions makers (e.g. critical resources like hospital beds,
ambulances, etc.)
D. And more…
2020, © All rights reserved.

13. Fighting Coronavirus Impacts with Location Analytics
2020, © All rights reserved.
Demo case inspired by an article in The Guardian last week:
• Looking for pandemic trends in time
• Establishing correlations with
• Socio-economic data
• Population density
• Transport occupancy
• Getting a sense of Public Hospital exposure
Montréal, Canada

14. DEMO

15. Data, Location & Analytics
To Find Solutions To Your Questions
A. I need to help answer location related pandemic questions (e.g.
deconfinement management)
B. I need to map my own ratios/measures and compare to
government data
C. I need to provide operational proximity data to field forces or
decisions makers (e.g. critical resources like hospital beds,
ambulances, etc.)
D. And more…
2020, © All rights reserved.

16. Pandemic Data Segmentation Analysis
• Unsupervised learning techniques to find natural groupings and data
patterns
• Clusters are formed such that objects in the same cluster are similar, and objects in
different clusters are distinct
• Data source is by French administrative department once a day
• Input data is only from Hospitals (no retirement homes), by day:
• Available ICU beds
• Hosp (# Hospitalized Patients)
• ICU (# Hospitalized Patients in ICU)
• Incid_Dc (# Incidence* of Deceased Patients)
• Incid_Hosp (# Incidence of Hospitalized Patients)
• Incid_ICU (# Incidence of Hospitalized Patients in ICU)
• Incid_Recovered (# Incidence of Recovered Patient)
*not variation, new events/cases
2020, © All rights reserved.

17. Results: 7 groups and their characteristics every day
• Group 1 describes a normal hospital situation
• Group 7 describes a Covid saturated ICU situation
• Group 4 is the inflexion point, means situation can improve or worsen
• But where is this all happening?
• What is the geographic effect?
• Does it confirm what is happening on the field?
2020, © All rights reserved.
Values = Category Avg. over Overall Avg. (Base = 100)
Groups, Hospital Covid Cases Available ICU Beds Hosp ICU Incid-Dc Incid_Hosp Incid_ICU Incid_Recovered
1 - green zone, no cases 39 34 32 21 30 31 31
2 - green zone, some cases 106 94 92 79 69 52 84
3 - rapide decrease in cases 109 78 110 214 357
4 - mixed, pivot point 191 204 193 171 129 183
5 - rapid increase in cases 233 194 206 401 404 359 317
6 - decrease after saturation of cases 219 395 384 341 248 236 354
7 - saturation 219 380 418 777 725 727 503
Output Variables

18. Confinement analysis and hospital capacity:
From bad to worse to better
• Map by Group as it evolved in time
• Looking for inflexion point pattern, will there be a 2nd wave?
• Can be carried at any level having data
Beginning of confinement (March 17) End of confinement (May 11)Worst day (April 10)
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19. Galigeo Location Analytics Software
A solution for every business case
Platform • Embedded • API
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20. Galigeo for
Salesforce
Galigeo
Cloud
Galigeo SAP
BI
Galigeo for
Cognos
Galigeo
API
Optional
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21. Harness all the potential of
Location Analytics today
Try out our solutions for 30 days for free
2020, © All rights reserved.
Looking for a tailored demo based on your data? Contact us.

22. Do not hesitate to get in touch
2020, © All rights reserved.
Christian Tapia
Business Development
ctapia@galigeo.com
Vincent Dechandon
Product Manager
vdechandon@galigeo.com
www.galigeo.com
@galigeo #galigeo
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