This document provides an overview of state-of-the-art geospatial technologies for health applications. It discusses how geospatial data is collected through methods like geocoding, GPS, surveys, social media, sensors, and secondary data sources. Applications of geospatial data in health include analyzing spatial and space-time patterns of diseases, measuring geographic accessibility to healthcare services, mapping exposures, and identifying opportunities with new data sources like big data. Moving forward, opportunities exist in utilizing more disaggregated data, improving uncertainty analysis, and enhancing public health surveillance globally.

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. 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. 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. 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. 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. Data Collection Geocoding
Geocoding
• Process of converting addresses to geographic coordinates
Eric Delmelle Geospatial Technologies for Health October 23 2018 6 / 43

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. 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. Data Collection Geocoding
Geocoding
• Preserving privacy but maintaining spatial patterns
Eric Delmelle Geospatial Technologies for Health October 23 2018 9 / 43

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. 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. Data Collection GPS
Global Position System - continued
• Interesting routes on Strava
Eric Delmelle Geospatial Technologies for Health October 23 2018 12 / 43

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. 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. 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. 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. 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. 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. 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. 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. 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. Applications Pattern Analysis
Space-time patterns - continued
Eric Delmelle Geospatial Technologies for Health October 23 2018 22 / 43

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. Applications Pattern Analysis
Space-time cluster detection - continued
• Visualizing clusters
Eric Delmelle Geospatial Technologies for Health October 23 2018 24 / 43

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. 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. Applications Accessibility
Geographic Accessibility - continued
• Long and short travel (different catchment areas)
Eric Delmelle Geospatial Technologies for Health October 23 2018 27 / 43

28. Applications Accessibility
Geographic Accessibility - continued
Eric Delmelle Geospatial Technologies for Health October 23 2018 28 / 43

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. 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. 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. Applications Accessibility
Residential mobility
• Impact on travel estimates
Eric Delmelle Geospatial Technologies for Health October 23 2018 32 / 43

33. Applications Accessibility
Medical deserts
• Identify regions with shortage of healthcare providers
Eric Delmelle Geospatial Technologies for Health October 23 2018 33 / 43

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. Applications Accessibility
Food deserts
• Application to North Carolina
Eric Delmelle Geospatial Technologies for Health October 23 2018 35 / 43

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. 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. 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. 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. 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. Thank you
Thank you
• eric.delmelle@uncc.edu
• Welcome comments
Eric Delmelle Geospatial Technologies for Health October 23 2018 41 / 43

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. 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