IMPROn järjestämässä Paikkatieto sote-uudistuksen tukena seminaarissa 8.10.2019 Pohjois-Carolinan yliopiston Eric Delmelle esitteli nousevia teemoja terveysmaantieteen saralla. Esityksen keskiössä on uusien teknologioiden ja niiden datan hyödyntäminen terveysmaantieteessä.

1. Geospatial Health
Emerging themes
Eric Delmelle / University of North Carolina at Charlotte
October 8 2019 /

2. Contents
 Some definitions and context
 Popularity of health among geographers
 Emerging themes
 environmental exposure
 space-time accessibility
 infectious diseases
 real-time predictions
 Outlook

3. Health Geography
 A geographical approach allows the examination of
health from a spatial perspective and through a
place lens
 The spatial perspective emphasizes how and why
health risks and disease burdens are spatially
distributed and connected the way they are
 The place lens supports the investigation of how
the social, cultural, economic, and physical
environments interact with people within a specific
environment to shape the health of its population

4. Popularity

5. Popularity: AAG

10. Opportunities
 Geospatial health offers unique opportunities and
challenges for understanding health issues
 Important methodologic contributions
 The potentials of geographic information system (GIS) to
analyse and visualize disease and risk patterns
 Location-enabled online services and social media
 Volunteered geographic Information (VGI)
 Portable sensors
 GPS and tracking/locational technologies

11. Opportunities
 Internet has changed the landscape of public health
surveillance and epidemic intelligence gathering
 Continuous development in location acquisition
and communication technologies and sensor data
 Examine environment exposure and health risk at a much
finer scale and (near) real-time.
 GIS can be aligned with global positioning system (GPS)
to monitor and analyse the movement of people and their
interaction with environment for health studies.

12. enabling technologies
 Social media change health and
medicine
 Smartphones are effective across
a range of social learning and
communication
 Patient care
 Monitoring
 Rehabilitation
 Diagnosis
 Communication (dissemination of
information to public)

13. enabling technologies

14. enabling technologies
 Geospatial technologies play a pivotal role in
augmenting traditional health data
 Geotagged social media data
 Empirical studies have successfully embraced such
data into the geospatial analyses of health issues
 Spatial patterns of depression among population
 Neighbourhood happiness, diet, and physical activity
 Pokemon Go

15. environmental Exposure
 Asthma & GIS
 Water pollution
 Flint, Michigan (lead, arsenic)
 Smoking exposure (second hand)
 Mining exposure to Sillica
 Pesticide and birth defects
 Environmental justice
 Greater awareness in the community

16. environmental Exposure
 Exploring the effect of air pollution on social
activity in China using geotagged social media
check-in data
Effect of air pollution on
urban activity.
Effect exists and varies
between pollutants,
visitors and residents,
and different activity
types.

17. environmental Exposure
 Population exposure to air pollution using
individual mobility patterns (from mobile data)
Use population activity patterns
representing several million
people to evaluate population-
weighted exposure to air pollution
in NYC
Population-weighted exposure to
PM2.5 using population activity
patterns and spatiotemporal
PM2.5 concentration levels

18. environmental Exposure
 Space-Time exposure to ozone concentration
Estimates could be erroneous when spatiotemporal variability of air pollution
and human mobility are ignored
(simulated)

19. environmental Exposure (simulated)

20. environmental Exposure

21. environmental Exposure
 Citizen science-derived data for modeling of PM2.5
Citizen
science-
derived data
Creation of a
land-use
regression
model for PM
2.5 and PM
coarse for a
vulnerable
community

22. environmental Exposure
 Patterns of CO2 emissions by taxis
Individuals'
travel
behavior
Use of taxi
trajectory
data

23. environmental Exposure
 Self monitoring of air pollution?
Accurate and straightforward
portable equipment.
Exposure misclassification
inherent in the fixed site
measurement stations and can
be addressed using a
combination of personal
exposure assessment and
modelling.

24. environmental Exposure
 Measuring stress
Monitoring heartbeat
But is it due to stress
or excitement?
Context?
Can this information
be useful to make our
cities less stressful?

25. environmental Exposure
 Measuring stress
https://gcmillar.githu
b.io/stress3d/

26. environmental Exposure
 Measuring stress
Substance users’
exposures to
environmental
stress (crime and
SES)
Methadone

27. environmental Exposure
 Noise pollution
Crowdsourced
big data (311)
Urban
development
Noise
annoyance.

28. good Exposure
 Proximity to green areas -> health outcomes?
NDVI
Buffer sizes
Survey
questionnaires

29. monitoring Access
 Access to public parks and satisfaction

30. monitoring Access
 Access to food in space and time

31. monitoring Access
 Access to food in space and time

32. Food satisfaction
 Food environment influence food choices?

33. Infectious diseases
 Human Mobility and the Onsets of Influenza Illness
Individuals with app reports on symptoms and also ask on location

34. Infectious diseases
 Usefulness of twitter?
Ebola related tweets, March 2015

35. Infectious diseases
 Google searches can signal outbreak

36. Real-time predictions?
 Flu near you: https://flunearyou.org/#!/

37. Real-time predictions?

38. Real-time predictions?
 Global flu: www.globalfluview.org/map
Individuals
report
symptoms in
real-time
Complement
traditional
influenza
tracking

39. Real-time predictions?
 HealthMap www.healthmap.org
Disparate data
sources
• online news
• Aggregators
• eyewitness
reports
• expert-curated
discussions
• validated official
reports

40. Real-time predictions Challenges
 data needs
 epidemic surveillance, mobility, host and environmental
susceptibility, pathogen transmissibility, population
density, and healthcare capacity.
 constraints (affects precision)
 timely data sharing
 standardized case definitions
 resource-limited settings
 lack of granular data available
 incorporate novel data streams

41. Conclusions & Outlook
 this all looks very nice but…
 Is the information provided useful in a decision-making
context?
 environmental health (monitoring pollution peaks) and
alerting citizens
 for you this can be…
 encouraging feedback (e.g. steps)
 therapist (tracking your physical effort)

42. Conclusions & Outlook
 More work necessary to evaluate usefulness of
these new technologies
 Uncertainty of collected data
 Content
 Sarcasm
 Data sharing
 Geospatial data enclave