1. Mapping spatial patterns of people’s risk perception of landslides
Christian Kofler1, Lydia Pedoth1, Agnieszka Elzbieta Stawinoga1, Stefan Schneiderbauer1
1European Academy of Bolzano/Bozen, Viale Druso 1, Bolzano/Bozen, Italy

2. Background & research questions
Community resilience against natural hazards is influenced by how individuals perceive risk and behave in threatening situations
Is it possible to measure risk perception and risk behaviour?
Do risk perception and behaviour have a spatial dimension?
Are they a function of spatial distance to the event?
Starting from the assumption that community resilience against natural hazards is influenced by how individuals perceive risk and…
We asked ourselves if it is possible to measure risk perception…
If risk p. and behaviour have a spatial dimension and if

3. Case study area
Municipality of Badia/Abtei, South Tyrol, Northern Italy
3458 Inhabitants (ASTAT, 2015)
Landslide in December 2012
Heavy precipitation events in November
Slope started to slide at its clay-rich lower end
Damage:
Four residential buildings were completely destroyed
Damaged infrastructure (roads, power lines,…)
36 people evacuated
We carried out a case study in South Tyrol

4. Data Questionnaire survey conducted in April 2014
2523 questionnaires distributed among all adults
Response rate of 43% (1096 questionnaires)
Questionnaire design:
5 sections: Risk perception, intervention, protection measures, social networks, needs for improvement
Demographic data including residential zone in respect to the location of the landslide as spatial information
We conducted a questionnaire survey among the population of the valley:
2523 questionnaires were distributed,…
The questionnaire was structured in 5 sections containing questions regarding….
Six residential zones were our spatial information

5. Application on residential zones
Methodology (1/3)
Univariate statistical analysis  Frequency distributions
Multivariate statistical analysis  Cluster analysis, Principal component analysis (PCA)
Mapping of results according to residential zones
Questionnaire data
Frequency
Distribution (not discussed today)
Cluster
analysis
Principal
component
analysis
Risk behaviour profiles
Risk perception factor
Based on the questionnaire data we performed different statistical analyses of both univariate and multivariate character
Then we applied the results to the residential zones
Now I’ll explain the statistical methods more in detail…
Application on residential zones
Maps

6. Methodology (2/3) Risk behaviour profiles
Cluster Analysis  Risk behaviour profiles
Definition: Cluster analysis is the task of grouping a set of objects in such a way that objects in the same cluster are more similar to each other than to those in other groups (clusters).
Why? To identify groups of respondents that behave in a similar manner
Clustering of responses regarding (selected qualitatively):
personal experience of landslides,
active participation at clean-up works,
knowledge of landslides,
safety feeling
Applied clustering method: Two-step clustering
Cluster analysis to identify RB profiles
Risk behaviour profiles

7. Risk perception factor?
Methodology (3/3)
PCA  Risk perception factor
Definition: PCA to express complex concepts that cannot be explained with one single variable (Werner, 2014)
Why?
RP can‘t be measured with one question (variable)
To group questions to one expressive factor that explains RP
Input: All questions of questionnaire section: risk perception and protection measures (see p.4)
To pass from Likert scale to metric scale  arithmetic mean to measure average perception of risk
PCA used to express complex concepts that cannot be explained by only on variable
Input for factor analysis were all questions of questionnaire section: RP and protection measures
Questionnaire questions: Likert scale 1-5  to pass on a metric scale: average between all selected questions
Risk perception factor?

8. Results (1/3) Risk behaviour profiles:
Four clusters identified (Average Silhouette = 0.6, Good):
“Aware but not concerned” (biggest cluster)
“Experienced and concerned”
“Not aware but concerned”
“Active, aware and concerned”
Spatial dimension:
Zone 1: Differs from other zones
Other zones: “Aware, not concerned” biggest cluster
“Experienced and concerned” rises in outer zones
Results regarding Cluster Analysis:
We identified four clusters of respondents and gave them names according to their characteristics:
Aware but not concerned: people know that their valley is exposed to LS but don’t feel threatened. People living at the valley bottom
Experienced and concerned: People experienced LS personally and feel threatened
Not aware but concerned: People had no knowledge regarding LS and feel threatened since LS event in 2012
Active, aware, concerned: The only cluster where people were actively involved in cleanup works. They feel highly threatened since 2012
Look at spatial distribution: Zone 1 differs significantly from other zones:
“Experienced and concerned is biggest cluster (relative terms)”
“Active aware and concerned” is second biggest cluster
- Other zones: “Aware but not concerned” is biggest cluster

9. Results (2/3) Risk perception factor
Eight questions are summarized to one factor on a metric scale from 1(low) – 5(high)
Observed range of values :
Spatial dimension:
Zone 1 highest score  RP
No linear decrease with distance
RP Factor
Possibility of an event of this extent?
I feel threatened since the event!
How likely are future damages at your building?
How likely is a future limiting of mobility?
How likely is a future evacuation?
How likely are other kinds of property damages?
How likely are other kinds of losses/limitations?
How safe you feel after protection measures?
These eight questions were found to be suitable to express risk perception
Yellow ring seems green

10. Results (3/3) Validation of Risk perception factor
To measure if there are significant differences of risk perception factors in different zones  One-way ANOVA
We wanted to make sure that this finding is not by chance so validated the risk perception factor by means of a one-way ANOVA test and the result was that RP factor differs in zone 1 significantly from all other zones.

11. Research questions vs. conclusion
Is it possible to measure risk perception and behaviour?
Do risk perception and behaviour have a spatial dimension?
Are they a function of spatial distance to an event?
Yes, questionnaire surveys allow to get an insight of how people perceive risk and behave in threatening situations
The only spatial pattern we could identify was that zone 1 differed significantly from the other zones in terms of active participation, feeling threatened and perception of risk. We cannot confirm that RP and RB are expressible in units of distance measure

12. Conclusion, general remarks
Low spatial resolution main shortcoming
Immediate proximity to event lets people take action:
Valid only for a rural society?
individual responsibility
…but they feel more threatened  what does it mean in terms of resilience?
Risk behaviour profiles can help allocating resources in a more efficient way to needs of certain target groups
Both metrics (RB profiles, RP Factor) can help understanding how resilient certain societal groups are
Low spatial resolution was main limiting factor of study
First zone was different for both metrics:
Perception of risk is higher
But also the willing of individuals to take action (is this because of the rural, homogenous structure of the village?)
People that were actively involved feel more threatened  good or bad in terms of resilience?

13. Thanks for your attention!
For more information feel free to contact me:
or visit: embrace-eu.org/
This research was part of the emBRACE-project and has been funded by the Seventh Framework Programme (FP7) of the European Union.
Furthermore, we want to thank the Geological Office of the Autonomous Province of Bozen/Bolzano and the Municipality of Badia for their support.