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Eric Delmelle: State-of-the Art in Geospatial Technologies for Health

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Geospatial data in health and welfare research -seminar in Helsinki 23rd October 2018.

Published in: Health & Medicine
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Eric Delmelle: State-of-the Art in Geospatial Technologies for Health

  1. 1. State-of-the Art in Geospatial Technologies for Health Eric Delmelle University of North Carolina, Charlotte, U.S.A. October 23 2018 Eric Delmelle Geospatial Technologies for Health October 23 2018 1 / 43
  2. 2. Introduction Importance of Maps and Health Motivation • Geography and Health have been intertwined for a long time Eric Delmelle Geospatial Technologies for Health October 23 2018 2 / 43
  3. 3. Introduction Importance of Maps and Health Motivation • London cholera outbreak and the map of John Snow Eric Delmelle Geospatial Technologies for Health October 23 2018 3 / 43
  4. 4. Introduction Importance of Maps and Health Uptake of GIS in the 1980,90s 1 Uptake of GIS in 1980s and 1990s • Health atlases are being developed. • GIS is merely used to automate mapping. 2 GIS gradually used for research after 2000s, when various spatial analytical functionality are added to commercial GIS softwares. 3 A turn for geospatial health around 2000-2010s • Census data becomes increasingly available • Geospatial data acquisition technologies • Highly accurate spatial (and temporal) data Eric Delmelle Geospatial Technologies for Health October 23 2018 4 / 43
  5. 5. Introduction Importance of Maps and Health What is a GIS? • Store, share, analyze, and visualize spatial data • Integration of multiple layers of interdisciplinary spatial data Eric Delmelle Geospatial Technologies for Health October 23 2018 5 / 43
  6. 6. Data Collection Geocoding Geocoding • Process of converting addresses to geographic coordinates Eric Delmelle Geospatial Technologies for Health October 23 2018 6 / 43
  7. 7. Data Collection Geocoding Geocoding • Illustration to private water wells near Charlotte, NC Eric Delmelle Geospatial Technologies for Health October 23 2018 7 / 43
  8. 8. Data Collection Geocoding Geocoding • How good are the geocodes? On-line geocoders? • Is it safe (from a privacy perspective) to use on-line? !( !( !( !( !(!(!( !(!( !( !( !( !( !( !( !( !(!( !( !( !( !( !( !( !( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!(!( !( !( !( !( !( !( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !(!( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( Copyright:© 2013 ESRI, i-cubed, GeoEye !( !( !( ght:© 2013 ESRI, i- GeoEye !( !( !( !( !( !( Copyright:© 2013 ESRI, i-cubed, GeoEye E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E EE E E E E E E E E E E E E E E E E E E EE E EE E E E E E E E E E E E E E E E E E E E E EE E EE E E E E E E E E EE E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E EEEE E E E E E E E E E E E E E E E E E E E E E E E E E E E EE E E E EE E E EE E E E E E E E E E E E E E E E E EE E E EE E E E E E E E E E E E EEE EE EE E E E E E E E E E E E E E E E E EE E EEE E E E E E E !( !( !( !( !( !( Copyright:© 2013 ESRI, i- ubed, GeoEye 10 - 25 !( < 10 !( 25 - 50 !( 50 - 100 !( > 100 !( A B C D A B C D E F well > 10 < 0.1 0.1 – 0.5 0.5 – 0.75 0.75 – 1 1 – 1.34 1.34 – 2 2 – 10 E !( commercial !( mapquest !( google error distance (mapquest) error distance (google) Eric Delmelle Geospatial Technologies for Health October 23 2018 8 / 43
  9. 9. Data Collection Geocoding Geocoding • Preserving privacy but maintaining spatial patterns Eric Delmelle Geospatial Technologies for Health October 23 2018 9 / 43
  10. 10. Data Collection GPS Global Position System (GPS) • Used in many field-based studies (e.g. sampling, ecology) Eric Delmelle Geospatial Technologies for Health October 23 2018 10 / 43
  11. 11. Data Collection GPS Global Position System - continued • Incorporated in many personal devices • Walking, running, bicycling Eric Delmelle Geospatial Technologies for Health October 23 2018 11 / 43
  12. 12. Data Collection GPS Global Position System - continued • Interesting routes on Strava Eric Delmelle Geospatial Technologies for Health October 23 2018 12 / 43
  13. 13. Data Collection Surveys Surveys • Qualitative information can be obtained by means of surveys • Why do individuals choose a particular health facility? • Why do individuals tend to utilize a public park more than another? Eric Delmelle Geospatial Technologies for Health October 23 2018 13 / 43
  14. 14. Data Collection Social Media Social Media • Twitter, Google Rating, Instagram • Identify early epidemics Eric Delmelle Geospatial Technologies for Health October 23 2018 14 / 43
  15. 15. Data Collection Social Media Social Media - continued • Content analysis - what are individuals’ feelings • Word clouds, valence and arousal Eric Delmelle Geospatial Technologies for Health October 23 2018 15 / 43
  16. 16. Data Collection Sensors data Sensors data • Monitor air pollution and map its variation Eric Delmelle Geospatial Technologies for Health October 23 2018 16 / 43
  17. 17. Data Collection Sensors data Sensors data - continued • Non-intrusive devices to measure exposure or other conditions Eric Delmelle Geospatial Technologies for Health October 23 2018 17 / 43
  18. 18. Data Collection Aggregated data Aggregated data • Most health data is aggregated - different scales Eric Delmelle Geospatial Technologies for Health October 23 2018 18 / 43
  19. 19. Data Collection Secondary data Secondary data • Elevation, vegetation cover, land-use, road networks.... • Citizen powered data such as open street map Eric Delmelle Geospatial Technologies for Health October 23 2018 19 / 43
  20. 20. Applications Pattern Analysis Spatial patterns • Does the data exhibit spatial patterns? • Indicative that disease may occur at a larger rate in specific regions • Influence prevention measures and efforts to curb disease Eric Delmelle Geospatial Technologies for Health October 23 2018 20 / 43
  21. 21. Applications Pattern Analysis Space-time patterns • Spatial methods ignore temporal dimension • Duration and intensity of the pattern Eric Delmelle Geospatial Technologies for Health October 23 2018 21 / 43
  22. 22. Applications Pattern Analysis Space-time patterns - continued Eric Delmelle Geospatial Technologies for Health October 23 2018 22 / 43
  23. 23. Applications Pattern Analysis Space-time cluster detection • Satscan (Kulldorf), Geoda (Anselin) • Cluster and relative risk Eric Delmelle Geospatial Technologies for Health October 23 2018 23 / 43
  24. 24. Applications Pattern Analysis Space-time cluster detection - continued • Visualizing clusters Eric Delmelle Geospatial Technologies for Health October 23 2018 24 / 43
  25. 25. Applications Pattern Analysis Space-time cluster detection - continued • Visualizing Chikungunya clusters in a 3D framework Eric Delmelle Geospatial Technologies for Health October 23 2018 25 / 43
  26. 26. Applications Accessibility Geographic Accessibility • Access has several dimensions • Potential versus realized access (disparities, poor coverage) Eric Delmelle Geospatial Technologies for Health October 23 2018 26 / 43
  27. 27. Applications Accessibility Geographic Accessibility - continued • Long and short travel (different catchment areas) Eric Delmelle Geospatial Technologies for Health October 23 2018 27 / 43
  28. 28. Applications Accessibility Geographic Accessibility - continued Eric Delmelle Geospatial Technologies for Health October 23 2018 28 / 43
  29. 29. Applications Accessibility Geographic Accessibility - continued • Travel impedance can be aggregated • Difference between realized and potential - are there clusters? Eric Delmelle Geospatial Technologies for Health October 23 2018 29 / 43
  30. 30. Applications Accessibility Geographic Accessibility - continued • Access can also be represented in a raster format Eric Delmelle Geospatial Technologies for Health October 23 2018 30 / 43
  31. 31. Applications Accessibility Residential mobility • Residential location of patient assumed fixed in many studies first diagnostic true residence at repeat diagnostic incorrect residence at repeat diagnostic same residence at first and repeat diagnostic repeater ignoring residential mobility accounting for residential mobility temporal signature residence time x y geographic unit (e.g. census tract) residential mobility 2 0 2 1 1 0 1 4 0 1 0 0 number of cases per geographic unit without residential mobility with residential mobility+ A B potential cluster cluster 1 2 3 4 5 6 Eric Delmelle Geospatial Technologies for Health October 23 2018 31 / 43
  32. 32. Applications Accessibility Residential mobility • Impact on travel estimates Eric Delmelle Geospatial Technologies for Health October 23 2018 32 / 43
  33. 33. Applications Accessibility Medical deserts • Identify regions with shortage of healthcare providers Eric Delmelle Geospatial Technologies for Health October 23 2018 33 / 43
  34. 34. Applications Accessibility Food deserts • Regions with shortage of grocery stores & healthy food options Eric Delmelle Geospatial Technologies for Health October 23 2018 34 / 43
  35. 35. Applications Accessibility Food deserts • Application to North Carolina Eric Delmelle Geospatial Technologies for Health October 23 2018 35 / 43
  36. 36. Applications Accessibility Access to public parks • Many benefits (physical exercise, mental health) Eric Delmelle Geospatial Technologies for Health October 23 2018 36 / 43
  37. 37. Applications Exposure GPS and exposure to air pollution • Movement of individuals (space-time context) - M.-P. Kwan Eric Delmelle Geospatial Technologies for Health October 23 2018 37 / 43
  38. 38. Applications Exposure GIS for mapping pollution • Finer spatial resolution over time to understand the relationship of health and pollution Eric Delmelle Geospatial Technologies for Health October 23 2018 38 / 43
  39. 39. Applications Exposure GIS for mapping pollution - continued • Map contaminants in the soil, or in the water (geostatistics) Eric Delmelle Geospatial Technologies for Health October 23 2018 39 / 43
  40. 40. Moving forward Opportunities • Big data (incl. social media), CyberGIS, HPC • Dissagregated data, tracking histories • Impact of uncertainty on statistical analyses • Transparency between scientists and stakeholders • Improve public health surveillance in disadvantaged nations Eric Delmelle Geospatial Technologies for Health October 23 2018 40 / 43
  41. 41. Thank you Thank you • eric.delmelle@uncc.edu • Welcome comments Eric Delmelle Geospatial Technologies for Health October 23 2018 41 / 43
  42. 42. References References • Casas, I., Delmelle, E., & Delmelle, E. C. (2017). Potential versus revealed access to care during a dengue fever outbreak. Journal of Transport & Health, 4, 18-29. • Desjardins, M. R., Whiteman, A., Casas, I., & Delmelle, E. (2018). Space-time clusters and co-occurrence of chikungunya and dengue fever in Colombia from 2015 to 2016. Acta tropica, 18 • Delmelle, E. M., Cassell, C. H., Dony, C., Radcliff, E., Tanner, J. P., Siffel, C., & Kirby, R. S. (2013). Modeling travel impedance to medical care for children with birth defects using Geographic Information Systems. Birth Defects Research Part A: Clinical and Molecular Teratology, 97(10), 673-684. • Delmelle, E. M., Zhu, H., Tang, W., & Casas, I. (2014). A web-based geospatial toolkit for the monitoring of dengue fever. Applied Geography, 52, 144-152. • Delmelle, E., Dony, C., Casas, I., Jia, M., & Tang, W. (2014). Visualizing the impact of space-time uncertainties on dengue fever patterns. International Journal of Geographical Information Science, 28(5), 1107-1127. • Dony, C. C., Delmelle, E. M., & Delmelle, E. C. (2015). Re-conceptualizing accessibility to parks in multi-modal cities: a variable-width floating catchment area (VFCA) method. Landscape and Urban Planning, 143, 90-99. • Dewulf, B., Neutens, T., Lefebvre, W., Seynaeve, G., Vanpoucke, C., Beckx, C., & Van de Weghe, N. (2016). Dynamic assessment of exposure to air pollution using mobile phone data. International journal of health geographics, 15(1), 14. • Hohl, A., Delmelle, E., Tang, W., & Casas, I. (2016). Accelerating the discovery of space-time patterns of infectious diseases using parallel computing. Spatial and spatio-temporal epidemiology, 19, 10-20. • Kirby, R. S., Delmelle, E., & Eberth, J. M. (2017). Advances in spatial epidemiology and geographic information systems. Annals of epidemiology, 27(1), 1-9. Eric Delmelle Geospatial Technologies for Health October 23 2018 42 / 43
  43. 43. References References • Kotavaara, O., Antikainen, H., & Rusanen, J. (2013). Accessibility patterns: Finland case study. Europa XXI, 24, 111-127. • Maatta-Juntunen, H., Antikainen, H., Kotavaara, O., & Rusanen, J. (2011). Using GIS tools to estimate CO2 emissions related to the accessibility of large retail stores in the Oulu region, Finland. Journal of transport geography, 19(2), 346-354. • Nagar, R., Yuan, Q., Freifeld, C. C., Santillana, M., Nojima, A., Chunara, R., & Brownstein, J. S. (2014). A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives. Journal of medical Internet research, 16(10). • Owusu, C., Lan, Y., Zheng, M., Tang, W., & Delmelle, E. (2017). Geocoding Fundamentals and Associated Challenges. • Park, Y. M., & Kwan, M. P. (2017). Individual exposure estimates may be erroneous when spatiotemporal variability of air pollution and human mobility are ignored. Health & place, 43, 85-94. • Richardson, D. B., Volkow, N. D., Kwan, M. P., Kaplan, R. M., Goodchild, M. F., & Croyle, R. T. (2013). Spatial turn in health research. Science, 339(6126), 1390-1392. • Sugg, M. M., Fuhrmann, C. M., & Runkle, J. D. (2018). Temporal and spatial variation in personal ambient temperatures for outdoor working populations in the southeastern USA. International journal of biometeorology, 1-14. Eric Delmelle Geospatial Technologies for Health October 23 2018 43 / 43

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