This document summarizes research on using natural language processing to extract location information from text for applications in disaster response and earthquake early warning. It discusses the challenges of identifying place names, disambiguating locations, and georeferencing to assign coordinates. Machine learning models have achieved success rates over 90% for detecting spatial relation terms like "near" and "at" and predicting distance ranges. The research is further developing these models with more contextual data. It is part of projects on using sensor networks and citizen reporting to enable early earthquake warnings and inform emergency management.

1. Text Location Extraction Applications for Disasters and
Earthquake Early Warning
Dr Kristin Stock
Massey Geoinformatics Collaboratory

2. A man has collapsed and is
unconscious, we’re doing CPR
near the public toilets,
park in Millwater,
opposite the set of shops where
Millies is,
near Silverdale school
Just off Millwater Parkway
Metro Park

3. What’s the street name?

4. Natural language location descriptions
• People most often describe location using relative location descriptions
• The located object, relative to some reference object
• They use spatial relation terms, often prepositions
• Near, at, beside, next to, just off, outside, around
• Less commonly: south of, north of
• Rarely: specific distances
• Hardly ever: Address, Coordinates, or anything easily locatable

5. The Goal
• Road damage, outside St Marys
• Power lines down next to the
SkyTower
• Buildings damaged around
Britomart
Actual Coordinates that we
can map, put into
GPS/SatNav, analyse,
calculate distance to, etc.
Qualitative Quantitative

6. The Challenges
• Identifying place names
– Current tools (Named Entity Recognition) varying accuracy
• Disambiguating place names
– St Marys:
• 10 St Mary’s Churches in NZ
• 11 St Mary’s in Auckland
• Georeferencing place names
– Gazetteer – coverage (local, colloquial, historical names), accuracy

7. Injured person in building
beside Sky Tower
Sky tower is red star.
Where would you go to find
this person?
1
5
4
3
2
6
11 10
9
8
7
12
15
14
13

8. What are we doing?
• Machine learning models to detect these kinds of descriptions:
– place names are used in many ways that do not describe location
– spatial prepositions often used in other ways:
• “I have matches in my pocket”
• “She’s beside herself with anxiety”
• Success rates in 90s (higher for individual prepositions)

9. Predictive Models for Spatial Relation Terms
• Building ML regression models to predict distances associated with
certain spatial prepositions
• Single model that includes features to model semantics of prepositions
– 25% improvement over zero distance baseline
– Actual distances vary on scale:
• MAE 19m for urban data set (London), 43% within 10m, 93% within 50m
• MAE 785m for country wide data set (UK), 47% within 10m, 56% within 50m
• Modelling of specific prepositions (near, at, in)

10. Analysing
Distances
Associated
with Specific
Prepositions

11. BioWhere
• New MBIE funded-project
• Further developing the regression models for specific prepositions
• Adding more contextual information into the models
• Large volumes of collections data
• Also creating self-learning gazetteer
• Esp Māori place names, also historical, colloquial, changing names
• biowhere.org

12. A man has collapsed and is
unconscious, we’re doing CPR
near the public toilets,
park in Millwater,
opposite the set of shops where
Millies is,
near Silverdale school
Just off Millwater Parkway
Metro Park

13. Links to IP4: Disruptive Technologies
• Smart Cities: Methods to extract information from social media (citizen
as sensors) to inform disaster preparation and response.
• Autonomous Vehicles: Enabling human-vehicle natural language
interface:
– Supporting destination description using relative locations
– Conversational interfaces
– Context aware instructions
Take me to that
bookshop next to
the Art Gallery
please.
Sure, would you
like to go through
the park, or along
the coast?
Take me to that
bookshop next to
the Art Gallery
please.

14. Earthquake Early Warning Project
• Feasibility of using low-cost sensors for earthquake early warning
• Community of Practice:
– Workshops, webinars
• Workshops with members of the public to establish their
needs/opinions about EEW
• crisislab.org.nz/eew/

15. Research Team

16. Community of Practice Events
Presenter Topic
Prof Muki Haklay, UCL Citizen science and disaster risk reduction
Prof Yih-Min Wu, National Taiwan
University
Taiwan’s earthquake early warning system
Dr Sara McBride, USGS US West Coast earthquake early warning
system
Gabriel Low, Raspberry Shake’ Sensors in Schools
Andres Meira, OpenEEW and Václav
Kuna, Czech Academy of Science
Open Earthquake Early Warning:
Dr. Masumi Yamada, Kyoto University Earthquake Early Warning in Japan
EEW Team, Massey University Insights from the Project’s Community-
based Workshops
● Launch Workshop: expert
presentations plus small group
discussion
● Visit to Whakarongotai Marae
● Shared Values Workshop: semi-
structured group discussions on
aspirations, strengths
● Paraparaumu College Visit
● EEW at QuakeCoRE Annual
Meeting: workshop, poster,
networking
Webinars
Workshops
Blog
http://crisislab.org.nz/category/eew/

17. Engagements, Collaborations and Partnerships

18. Public Engagement
Workshops
• Group workshops
• Trust vs benefit
• Participants position
themselves
• Then explain
why/discuss views
• 8 workshops around NZ

19. Key Findings

20. The Wider EEW Project
• Four parts
• IoT and
telecommunications is
proceeding
• Prediction and modeling
will include some
location/text analysis
components.

21. Multi-Source, Multi-Model Deep Learning for Emergency Traffic
Management
• PhD student Nilani Algiriyage
• Has developed methods to extract What, When, Where from
combination of social media and news reports
• Clusters reports from both sources, then summarises key content
• Traffic flow estimation using object detection from CCTV footage
• Combining traffic flow modelling with event detection to look at traffic
redirection etc.