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Geospatial health - Emerging themes


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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.

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Geospatial health - Emerging themes

  1. 1. Geospatial Health Emerging themes Eric Delmelle / University of North Carolina at Charlotte October 8 2019 /
  2. 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. 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. 4. Popularity
  5. 5. Popularity: AAG
  6. 6. 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
  7. 7. 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.
  8. 8. 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)
  9. 9. enabling technologies
  10. 10. 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
  11. 11. 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
  12. 12. 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.
  13. 13. 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
  14. 14. environmental Exposure  Space-Time exposure to ozone concentration Estimates could be erroneous when spatiotemporal variability of air pollution and human mobility are ignored (simulated)
  15. 15. environmental Exposure (simulated)
  16. 16. environmental Exposure
  17. 17. 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
  18. 18. environmental Exposure  Patterns of CO2 emissions by taxis Individuals' travel behavior Use of taxi trajectory data
  19. 19. 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.
  20. 20. 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?
  21. 21. environmental Exposure  Measuring stress https://gcmillar.githu
  22. 22. environmental Exposure  Measuring stress Substance users’ exposures to environmental stress (crime and SES) Methadone
  23. 23. environmental Exposure  Noise pollution Crowdsourced big data (311) Urban development Noise annoyance.
  24. 24. good Exposure  Proximity to green areas -> health outcomes? NDVI Buffer sizes Survey questionnaires
  25. 25. monitoring Access  Access to public parks and satisfaction
  26. 26. monitoring Access  Access to food in space and time
  27. 27. monitoring Access  Access to food in space and time
  28. 28. Food satisfaction  Food environment influence food choices?
  29. 29. Infectious diseases  Human Mobility and the Onsets of Influenza Illness Individuals with app reports on symptoms and also ask on location
  30. 30. Infectious diseases  Usefulness of twitter? Ebola related tweets, March 2015
  31. 31. Infectious diseases  Google searches can signal outbreak
  32. 32. Real-time predictions?  Flu near you:!/
  33. 33. Real-time predictions?
  34. 34. Real-time predictions?  Global flu: Individuals report symptoms in real-time Complement traditional influenza tracking
  35. 35. Real-time predictions?  HealthMap Disparate data sources • online news • Aggregators • eyewitness reports • expert-curated discussions • validated official reports
  36. 36. 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
  37. 37. 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)
  38. 38. Conclusions & Outlook  More work necessary to evaluate usefulness of these new technologies  Uncertainty of collected data  Content  Sarcasm  Data sharing  Geospatial data enclave