Interaktívne webové mapy ako nástroj pre analýzu heterogénnych dát pre krízové riadenie
1. Radim ŠTAMPACH, Ph.D.
Prof. Dr. Milan KONEČNÝ, PhD.
21 January 2021
Dynamic mapping methods
oriented to risk and disaster
management in the era of Big
2. Dynamic mapping methods oriented to risk and
disaster management in the era of Big data
Laboratory on Geoinformatics and Cartography,
Department of Geography, Faculty of Science
• leader: Prof. Milan Konečný
• responsible to Ministry of Education, Czechia
Nanjing Normal University
Key Laboratory of Virtual Geographic Environment,
School of Geography Science
• leader: Prof. Jie Shen
• responsible to Ministry of Science and
3. Disasters in China
• China is a laboratory for research of catastrophic
events – both natural and man-made.
• Floods, landslides, earthquakes, droughts,
2008 Sichuan earthquake
• 69,000 people lost their lives (68,636 in Sichuan
• 374,176 injured,
• 18,222 missing.
5. Disasters in Czechia
Statistics of Integrated Rescue System for 2015:
• 19,685 fires,
• 21,330 traffic accidents,
• 6,693 leaks of dangerous substances,
• 55,928 technical accidents.
2014 explosion of ammunition stores in Vrbětice
• two people died, houndreds of people evacuated,
• whole area, fields and forests closed for owners for
• long pyrotechnical cleaning needed,
• total costs estimated to 40 mill. Euro.
7. Goals of project
Why Big data in disaster management?
• The amount of data is constantly increasing.
• Including data available for disaster management.
The goal is a dynamic generation of maps from
heterogenous data that will be used to support the
solution of crisis situation.
• Such amount of data is difficult:
• to verify
• to harmonise
• to analyse
8. Heterogenous data
• Data from various Czech institutions and companies
• Brno Municipality
• Hydrological Directorate of the Morava river
• Directorate of Road and Motorway Network in the
• O2 mobile operator
• Different topics:
• traffic data
• meteorological data
• air quality data
• hydrological data
• localization of mobile phones
9. • Data collected by volunteers (VGI data)
• Data verification needs to be solved
• Measured data
• Different characteristics
• Different density of sensor network
• Different time intervals of mesurements
• Different formats (CSV, XLS, XML, TXT…)
• Different ways of data providing
• Data must be harmonised into uniform structure
10. Procedure for solving the project
• Data are collected from various available sources -
measured by sensors and recorded by volunteers.
• A mobile application was created for data collection by
volunteers. It is also adapted for use in other projects or
for teaching students.
• The obtained data are verified, especially in the case of
data supplied by volunteers.
• The heterogeneous data are transformed into a uniform
• Patterns and anomalies are identified in the time series
of harmonized data.
11. • During the project, cognitive studies were performed to
design map symbology usable in managing various
crisis situations in Czechia and China.
• Harmonized data, including detected anomalies, are
displayed in the form of an interactive web map using
the proposed symbology.
• An interactive web map that shows data, including real-
time anomalies, can be used to analyze data and make
decisions in crisis situations.
Procedure for solving the project
12. Results of project
• System of Interactive map for disaster
• VGI based application for collection of geodata
• Cross-cultural cognitive testing for purpose of the
• International seminars
13. • Open application for
• System of verification of
• Tested during teaching of
VGI based application for collection of
15. Cross-cultural cognitive testing
• Maps for disaster management should work both in China and Czechia.
• Different cultures perceive symbolisation in different way.
• Cognitive tests were made with Chinese and Czech participants.
• Participants: 148 in China, 82 in Czechia
19. • Harmonisation of heterogenous data from various
sources (different characteristics, different formats,
different spatial and time resolution…)
• Heterogenous data are transformed to uniform
database structure inspired by ISO 19156 Geographic
information – Observations and measurements.
• Automatic calculations of aggregated values to
facilitate further data processing
• configurable by configuration file
• e.g., „once a day calculate 30 daily averages“, „calculate
moving 24-hour averages“
• possibility to set time windows (week, month…)
System of Interactive map for
20. System of Interactive map for
• Map compositon is prepared from harmonised data stored
• Possibility of interactive exploration of data.
• Set of time windows can be defined and aggregated values
(e.g., averages) can be calculated.
• Anomalies in time series are identified and visualised.
• It allows identification and analysis of abnormal
situation which could trigger a crisis (e.g., extreme
21. • Type of phenomenon
• Measurements (Circle)
• Flow rate
• Air polution
• Observations (Diamond)
• Dry vegetation
Desing of map symbols - shape
22. • Abnormality of the presented value
• Color-blind proof
Desing of map symbols - color
No Medium High
26. • Map symbols of stations are interactive.
• Chart with values of selected characteristic is
visualized after selection of symbol.
• Chart is also interactive.
• Values of chart can be explored by mouse.
• If symbol is selected that aggregates more stations, it is
possible to choose station whose chart will be
Interactive map symbols and chart
30. Time period
• Collected data are mostly longer or shorter time series.
• Longer time series are often better visualized by
aggregated values – e.g. daily average, monthly average –
instead by raw data of measurements.
• Aggregated values are automatically pre-calculated after
importing of data into database.
• The mostly used combinations of time periods (this month,
previous month, this week, last week…) and level of data
aggregation can be set by buttons.
• Any other period can be set manually by From date and To
• Anomalies in data are identified and visualised.
• It allows identification of abnormal phenomenon
which could trigger a crisis situation (e.g. extreme
• This is possible way how to deal with amount of Big
• To identify values that are possibly important.
• Anomalies are represented:
• by color of symbols,
• by red line in charts.
• Internal test environment using real project data was
used to test various detectors and parameters to detect
• Luminol – library for anomaly detection and correlation –
• Actually detector of anomalies "LinkedIn bitmap" is used
in system. But it can be changed.
39. • The possibility of integrating heterogeneous alternative
data sources into the crisis management process was
verified. Even very heterogeneous data can be
automatically harmonized, transformed into a uniform
structure and used for situation analysis.
• The method of validation of data collected by volunteers
and their usability in the crisis management
environment was verified. As part of this, an application
for collecting volunteer data was created.
• Suitable tools for recognizing patterns and anomalies in
data time series have been identified.
Benefits of the project
40. Benefits of the project
• Intercultural studies of the cognition of an interactive
web map were performed on participants from Czechia
and China. This is one of the first comparisons of the
general principles of cartographic expression and
verification of its comprehensibility in these two cultural
• The “Interactive Map for Crisis Management” system
was created in the form of a web map, which will enable
more efficient decision-making processes of crisis
• These results are not only applicable in the field of crisis