Assessing spatial accessibility to primary health care services in the Metropolitan Area of Madrid using big data and GIS by Gennaro Angiello and María Henar Salas-Olmedo
GPU Accelerated Natural Language Processing by Guillermo Molini
Similar to Assessing spatial accessibility to primary health care services in the Metropolitan Area of Madrid using big data and GIS by Gennaro Angiello and María Henar Salas-Olmedo
Bus Karo: Economic Opportunity through Public Transport ConnectivityWRI India
Similar to Assessing spatial accessibility to primary health care services in the Metropolitan Area of Madrid using big data and GIS by Gennaro Angiello and María Henar Salas-Olmedo (20)
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
Assessing spatial accessibility to primary health care services in the Metropolitan Area of Madrid using big data and GIS by Gennaro Angiello and María Henar Salas-Olmedo
1.
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
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)
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
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
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