Datathon
Kick-off Event1 November, 2016
Active Public SpaceAnalyze. Ideate. Propose.
Welcome and agenda for today
• What and why
• Topics and questions
• Data
• Schedule and setupLucia Štefánková
Deputy Director for Development
and PR at CERGE-EI
2
What and why
• What and why
• Topics and questions
• Data
• Schedule and setupIva Kleinová
Program Director
MA in Applied Economics at
CERGE-EI
3
What is the Datathon and why are we doing it?
Part of Economics Discovery
Hub and organized by MA in
Applied Economics program
through which we try to help
students improve their skills in
data analysis with impact
1
2
3
Give students an ability to test and
improve their data analytical skills
on real world data.
Give IPR and the city of Prague
some food for thought on what they
can improve.
Provide specific policy suggestions
and/or entrepreneurial ideas for
how to solve problems.
4
Who is organizing the Datahon?
With generous data and/or mentoring help from:
5
Who will be involved - roles?
Jury
Jury members will use the following criteria for evaluation:
- Using Lean Kick Box Canvas
- Feasibility
- Originality and innovation of the solution
Mentors
Mentors will help the teams with their work in various areas: working with data,
urbanism, idea generation, using lean canvas…Mentors will be available during
whole Saturday and on Sunday before lunch.
Organizers
Organizers will make sure that you have data to work with, topics to work on and
you are fed during the event. They will be there in case you need anything.
Who will be involved - names?
Jury
Gabriel Ahlfeldt (LSE)
Bernhardt Sommer (University of Applied Arts in Vienna)
Mathilde Marengo (IACC)
Lucia Štefánková (CERGE-EI)
Regina Loukotová (ARCHIP)
Igor Kovačevič (CCEA)
IPR representative (tbc)
Mentors
Working with data: Lucie Zapletalová (CERGE-EI), Mariia Kosar (CERGE-EI), Taras Hrendash
(CERGE-EI), Lukáš Makovský (IPR), Irakli Barbakadze (University of Tartu), Youjun Shin (University of
Tartu), Tomáš Křehlík (IES), Tomáš Zelinský (TUKE),
IBM Data Experience Platform: Michal Polena (IBM)
GIS data: Eliška Kyzlíková (IPR)
Urbanism: Karin Grohmannová (CCEA), David Neuhaus (ARCHIP), Bára Šimonová (ARCHIP),
Strategy and ideation: Yvette Vašourková (CCEA), Eva Hromádková (CERGE-EI)
Organizers
CERGE-EI organizational team: Maya Kopecká, Lucia Štefánková, Karolína Břízová, Callum Cope,
Neesha Kaur
Topics and questions
• What and why
• Topics and questions
• Data
• Schedule and setupYvette Vašourková
Founder
Centre for Central European
Architecture (CCEA)
8
Datathon: Active Public Space
9
The workshop aims:
to analyse city data from both perspectives - the Top-Down and the Bottom-
Up to develop ideas related to city data in order to improve the actual city
spaces.
How can data help to actively shape cities?
How can we activate public space by data usage?
How can citizens collect data and actively shape their city?
This workshop will focus on exploring new solutions and possibly directly
intervene on the city data of Prague.
Topic: How do we enable an active public space?
10
Think:
effect of limited parking zones,
multi-transport migration for work and
leisure,
guidebooks for business development,
home-office infrastructure,
cycling,
alternative transportation tools,
shared car system
public transportation neglect zones…
public transportation as complex system
Urban Movements
How do people move around the city for work and
leisure?
What are effective transportation tools for future
city?
How should we transform public transporation for
today needs?
Have some groups been privileged while others
neglected in their travels?
Will future developments in the way we work and
have fun affect our transportation needs?
Topic: How do we enable an active public space?
11
Think:
zones of greatest ethnic and cultural
diversity;
prediction of future gentrification and
possible cultural clash zones;
zones where city might want to impose
social housing;
creation of distributed refugee housing;
restaurant startup guides…
Diverse City
Is Prague ethnically, religiously,
socioeconomically and culturally diverse?
Where and where not?
Has Prague drab or diverse districts?
Should any specific lifestyles or economic
backgrounds be promoted? How and where?
How does safety factor into the diversity equation?
Can our data give us any clues?
How to balance varied situations in urban space?
Topic: How do we enable an active public space?
12
Bottom up impact
How can virtual distributed tools allow for a more
participative urbanism?
How can more data be generated?
What kind of data is currently needed but not collected
to solve the city’s problems?
What are some innovative ways to collect this
information?
How can citizens activate public space?
Think:
data from experimental projects;
shared infrastructure for business data
sharing; statistically significant,
unbiased data sources for public policy
making, and more.
Datathon: Follow up
13
The workshop aims:
to analyse city data from both perspectives - the Top-Down and the Bottom-
Up
to develop ideas related to city data in order to improve the actual city
spaces.
How can data help to actively shape cities?
How can we activate public space by data usage?
How can citizens collect data and actively shape their city?
This workshop will focus on exploring new solutions and possibly directly
intervene on the city data of Prague.
Workshop will be followed by a sitespecific intervention to
improve a public space in Prague.
The Datathon participants will have opportunity to develop a
proposal for an intervention scheme, based on their analysis
conducted during Datathon.
This intervention will be installed and its impact tested within
2-3 months after the Datathon event in a specific location in
Prague.
viaduktkreativni.cz
Data
• What and why
• Topics and questions
• Data
• Schedule and setup
Mariia Kosar
PhD student
CERGE-EI
14
The main unit of analysis will be 920 microdistricts in Prague
Praha 6
Praha 8
Praha 5
Praha 4
Praha 16
Praha 22
Praha 12
Praha 15
Praha 21
Praha 13
Praha 11
Praha 14
Praha 10
Praha 20
Praha 19
Praha 18
Praha 9
Praha 7
Praha 17
Praha 3
Praha 1
Praha 2
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Praha 6
Praha 5
Praha 4
Praha 8
Praha 12
Praha 10
Praha 20
Praha 22
Praha 9
Praha 14
Praha 13
Dubeč
Zličín
Praha 15
Praha 21
Zbraslav
Praha 11
Čakovice
Řeporyje
Vinoř
Praha 16
Lipence
Ďáblice
Praha 7
Libuš
Slivenec
Praha 3
Kunratice
Praha 1
Praha 19
Kolovraty
Běchovice
Troja
Újezd
Praha 18
Suchdol
Šeberov
KlánovicePraha 2
Královice
Nedvězí
Satalice
Velká Chuchle
Koloděje
Křeslice
Benice
Nebušice
Dolní Chabry
Praha 17
Dolní Počernice
Lochkov
Březiněves
Lysolaje
Dolní Měcholupy
Štěrboholy
Přední Kopanina
Petrovice
Legend
Správní obvody / Administrative districts
Městské části / Municipalities
ZSJ / Basic statistical units
15
We will have five main sources of data for each of these districtsWe will have five main sources of data for each of these districts
Type Source
Geographical +
public transport
data (2015)1
Census
data (2011)2
Real estate
data (12/2015)4
People
movement
data (2016)5
Description
Built environment: buildings and physical
structure, land topology and use, urban and
zoning plans, public transport access,
pollution, noise levels…
People: gender, age, education, religion,
nationality, citizenship, occupation,
employment, type of accommodation
(rent/own), migration, etc.
Property and services: average advertised
price of residential and commercial real
estate, detailed information about properties
and neighborhood amenities
Movement: where people live, work, spend
time during the day and night
• Prague planning
office
• Public transport
company
• Czech Statistical
office
• Online real estate
portal Sreality.cz
• O2, one of three
largest mobile
operators
Panel data on
population and
childbirth (2007,
2011, 2015)
3People: survey data on child gender,
frequency of birth, citizenship of parents,
rank of newborn baby, age of parents, number
of babies
• Czech Statistical
office
What will be the format?
Mentors will be available for:
- Excel
- R
- STATA
- IBM Data Science
Experience Platform
+ mentors for
urbanism and idea
generation
17
Which specific variables will be available?Which specific variables will be available?
18
Available
- Please see separate
document with list of
variables
- Actual datasets will
be provided on
Friday/Saturday at
the Datathon
- Note: all variables
will be translated
into English
On request
- Number of potential
variables extractable
from geo data is too large
- Therefore, some of this
data will be available on
request on Saturday
during the Datathon; one
of the mentors will
extract them based on
actual needs of teams
Schedule and setup
19
• What and why
• Topics and questions
• Data
• Schedule and setupMaya Kopecká
Marketing Manager, CERGE-EI
Datathon Chief Coordinator
What can I expect each day?
20
Friday, November 11:
Inspiration, team setup
- 13:30-19:00
- Keynote lectures and
small-size workshops
with 3 international
speakers
- Detailed run through
data and topics
- Formation of teams
- Teams start working
- Teams created!
Saturday, November
12: Work on data
- 9:00-19:00
- Groups present their
topics and ideas
- LEAN methodology
workshop
- Work in teams
- Support from
mentors
- Progress presentation
- Party at CERGE!
Sunday, November 13:
Finalize outputs
- 9:00-15:00
- Work in teams
- Support from mentors
- Preparation of final
presentation
- Presentation in front of
the jury
- Winners announced!
- 2 coffee breaks
- Ability to sleep over at
CERGE-EI
- Coffee and refreshments
available throught the day
- Lunch
- Pizza + beer dinner
- Ability to sleep over at
CERGE-EI
- Coffee and refreshments
available throught the day
- Lunch
Logistics
21
Sleeping EatingTechnology
• Wifi available
throughout the premises
• You may request to use
school computers to
access Stata in the
computer lab
• Most work will likely be
done on your own
laptop
• Ability to sleep over at
CERGE with other
students
• Classroom will be
available, so bring your
own mat + sleeping bag
• Showers are available (1
male, 1 female)
• Refreshments will be
available during the day
• Lunch on Saturday and
Sunday for participants
• Pizza + beer dinner
party in CERGE-EI club
on Saturday for
participants
• CERGE-EI is centrally
located, so it’s easy to
get many types of food
within 5 min walking
distance
Thank you!
22