https://www.bigdataspain.org/2016/program/fri-assessing-spatial-accessibility-primary-health-care-services-metropolitan-area-madrid-using-big-data-gis.html https://www.youtube.com/watch?v=DYhQy9oKV6k&list=PL6O3g23-p8Tr5eqnIIPdBD_8eE5JBDBik&index=52&t=3s

2. Assessing spatial accessibility
to health care services in Madrid
using big data and GIS
Gennaro Angiello TeMALab Laboratorio Territorio
Mobilità e Ambiente
Mª Henar Salas-Olmedo
UNIVERSITY OF NAPLES FEDERICO II
Department of Civil Engineering
COMPLUTENSE UNIVERSITY OF MADRID
Department of Human Geography

3. What is spatial
accessibility?

4. Spatial accessibility
 Potential for interactions
(Hansen, 1959)
 The ease and convenience of access to
spatially distributed activities
(van Wee and Geurs, 2016)
 In the health care domain:
the web of interactions between health
care facilities, transport and population
in health maintenance
(Giuliano, 2004)

5. Why assessing
spatial accessibility to
health care service is
important?

6.  Accessibility to health care services (HCS) is widely accepted
internationally as a key goal in meeting the health needs of
individuals
(United Nations, 2009)
 Planners are increasingly relying on accessibility analysis to
identify under-served areas and allocate human and financial
resources
 Poor accessibility to HCS => lower health care utilization =>
poorer health outcomes
(Lankila et al., 2016)
Spatial accessibility analysis

7.  Provide static picture of accessibility to
HCS
 Neglect daily fluctuations
 Health care facilities (service supply)
 Population location (service demand)
 Transport performance
Shortcomings of current analysis

8. Accessibility is dynamic!

9. Data
HEALTH
CARE
FACILITIES
Health care (addresses, activity hours,
and duration)
Portal de Salud de la
Comunidad de Madrid
(PSCAM)
Cadastral data
Instituto Nacional de
Estadistica (INE)
Geolocated tweets Twitter API
Transit stops, routes, and schedules (GTFS)
Consorcio Regional de
Transporte de Madrid
(CRTM)
Pedestrian networks OpenStreetMap (OSM)
Data
POPULATION
TRANSPORT

10. Health care facilities
opening hours

11. Health care facilities
Health care facility locations and study area

12. Health care facilities
08:00 – 09:00

13. Health care facilities
12:00 – 13:00

14. Health care facilities
18:00 – 19:00

15. Variation in transit
performance

16. From Google data to
dynamic transport network

17. Number of bus trips between 08:00AM and 09:00 AM
𝐶𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑣𝑖𝑡𝑦𝑖(𝑡 𝑜 − 𝑡1) =
𝑠 ∈ 𝐼 𝑡 𝑜<𝑡<𝑡1
𝑡𝑟𝑖𝑝𝑠,𝑡

18. Variation in population
location

19. FromTwitter and cadastral data
to dynamic population location
08:00 – 09:00 AM

20. FromTwitter and cadastral data
to dynamic population location
12:00 – 13:00 AM

21. FromTwitter and cadastral data
to dynamic population location
18:00 – 19:00

22. Combined effects

23. Combined effects

24. Toward smarter
solutions

25. Smarter solutions
08:00 – 09:00 AM

26. Smarter solutions
08:00 – 09:00 AM

27. Discussion and
conclusion

28. Discussion and conclusion
 Current accessibility analysis are static
 Big data and GIS can support health care practitioners
in delivering smarter and more sustainable health care
strategies
 From hard to soft planning measures (e.g. adapting
opening hours to fit user needs or adapting transit
frequencies)
 Bridging the implementation gap: connect academia
with business and administration

29. Thanks
for your attention
Gennaro Angiello TeMALab Laboratorio Territorio
Mobilità e Ambiente
Mª Henar Salas-Olmedo
gennaro.angiello@unina.it
msalas01@ucm.es

30. Gennaro Angiello
UNIVERSITY OF NAPLES FEDERICO II
Department of Civil Engineering
COMPLUTENSE UNIVERSITY OF MADRID
Department of Human Geography