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

1. Multimodal accessibility of primary
health care and social services
Ossi Kotavaara1, Timo Pohjosenperä2
1Geography Research Unit, 2Oulu Business School
University of Oulu
Geospatial data in health and welfare research
23.10.2018 Helsinki
Low carbon logistics in local social
and health care services

2. BASELINE
Northern Ostrobothnia: 411 889 inhabitants
/ 391 365 reaches health center by car in 20 min
Greater* Oulu region: 261 420 inhabitants
/ 208 138 reaches health center by bus in 30 min
Travels in Oulu: 5 % bus / 16 % walk / 19 % bike /
60 % car
Oulu bus trips: 13168 / Feb 2018 / weekday 06-11
*Oulu, Kempele, Hailuoto, Ii, Liminka, Lumijoki, Muhos, Tyrnävä, Utajärvi
University of Oulu / Geography Reserach Unit / Geoinformatics

3. RESEARCH BACKGROUND
• The Finnish healthcare system is reorganization, new
service providers are accessing to emerging markets and
public authorities are responsible for adequate service
network.
• Adequate and equal access to services are key factors for
good-quality health care.
• Locally available services will also reduce the need for
passenger car travels and also CO² emissions, if public
transport may be easily used.
University of Oulu / Geography Reserach Unit / Geoinformatics

4. SERVICE ACCESSIBILITY / LOCATION-ALLOCATION
IN NORTHERN OSTROBOTHNIA
• Measuring accessibility and optimising service locations
/ GIS based computations
/ Bus and car travels
• Primary level services
/ Maternity and child health clinics / children
/ Health centres (primary health care) / all users
/ Dentits / all users
• Future needs of elderly health care
/ Emergency 24/7 and suplementary services
/ Primary health care hospitals and hospital wards
University of Oulu / Geography Reserach Unit / Geoinformatics

5. MEASURING TRANSPORT ACCESSIBILITY BY
GEOGRAPHIC INFORMATION SYSTEMS (GIS)
‘Accessibility... ...is the extent to which
the land-use transport system enables
(groups of) individuals or goods to reach
activities or destinations by means of a
(combination of) transport mode(s).’
Geurs & Ritseman van Eck (2001: 19)
University of Oulu / Geography Reserach Unit / Geoinformatics

6. ACCESSIBILITY BY CAR

7. ACCESSIBILITY BY PUBLIC TRANSPORT

8. ACCESSIBILITY BY BUS AND CAR
IN GREATER OULU REGION AND NORTHERN OSTROBOTHNIA
University of Oulu / Geography Reserach Unit / Geoinformatics
Car
Bus

9. BUS
TRAVELS
/
FEBRUARY
WEEKDAY
06-11 AM
AVERAGE
/
GREATER
OULU
REGION

10. PROFILING BUS USERS
(OULU REGION / FEBRUARY 2018)
University of Oulu / Geography Reserach Unit / Geoinformatics
User groups by age (%) (N)
Children 20,0 179 969
Youth & Students 38,8 348 807
Adults 41,1 369 264
Sum 100 898 040
Ticket types (%) (N)
Season ticket 58,3 523 528
Value ticket (card) 24,0 215 431
Single ticket 11,9 106 982
School (childred) 5,8 52 099

11. P-MEDIAN / MINIMIZE IMPEDANCE
/ MOST EFFICIENT TRAVEL PATTERN
• P-median (i.e. minimize impedance) reduces the overall
distance the public needs to travel to reach the chosen
facilities
• Locating facilities with minimizing the overall need of traveling
/ Minimize Impedance chooses facilities such that the sum of
weighted impedances (demand allocated to a facility multiplied by
the impedance to the facility) is minimized
/ The minimize impedance is ordinarily regarded as more equitable
than other problem types for locating some public-sector facilities
such as libraries, regional airports, museums, department of motor
vehicles offices, and health clinics.
University of Oulu / Geography Reserach Unit / Geoinformatics

12. INTEGRATED PUBLIC TRANSPORT AND
PASSENGER CAR LOCATION-ALLOCATION
/ Potential bus users are routed from home locations to
all potential service locations via the fastest available
transit connections within a defined time period
/ By applying this connectivity matrix with car travel
routing, public transport and car travels may be
integrated in the location-allocation analysis.
University of Oulu / Geography Reserach Unit / Geoinformatics
walk +
bus with changes
+ walk

13. ROUTING PUBLIC TRANSPORT AND CAR USERS
Fastest connections between
each residential and service
location
Bus travels starting on
Tue 6. Feb 2018 8:00–8:59
/ One-minute intervals
/ Realistic travel chains
/ Connectivity matrix for
location allocation
University of Oulu / Geography Reserach Unit / Geoinformatics
walk +
bus with changes
+ walk

14. DATA FOR CAR AND PUBLIC TRANSPORT ROUTING
• Digiroad
• Regional and local main streets,
collector and feeder streets and
private roads allowed for public
use…
• Travel speed estimates were
formulated on the basis of the
speed limit data
• General Transit Feed Specification
(GTFS) of Oulu Region public transports
• Scheduled bus connections
• Travel chains
• 4 km/h was applied for walking
• Northern Finland is mainly uncongested
 turn penalties / urban delay rates

16. PUBLIC TRANSPORT AND CAR TRAVEL ESTIMATES
BY HOUSEHOLD CAR OWNERSHIP DATA
Car ownership data applied in
producing raw estimates for the
public transport users
/ Share of households having no
car  will updated with bus stop
specific data
~216 000 inhabitants divided to
~ 53 000 public transports users
~ 160 000 car users
University of Oulu / Geography Reserach Unit / Geoinformatics

17. DEVELOPING SERVICE NETWORK BY ACCESSIBILITY
P-MEDIAN LOCATION-ALLOCATION RESULTS
Allocation by bus and car Allocation by car, all users Allocation by bus only

18. REFERENCES
Ahmadi-Javid, A., Seyedi, P., & Syam, S. S. (2017) A survey of healthcare facility location. Computers & Operations Research, 79,
223-263.
Baray, J., and Cliquet, G. (2013) Optimizing locations through a maximum covering/p-median hierarchical model: Maternity
hospitals in France. Journal of Business Research 66(1), 127-132.
ESRI (2012) Algorithms used by Network Analyst. [Online]
http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#//004700000053000000. [Accessed 18 Jan 2018].
Finnish Road Administration, Digiroad (2017) Description of data objects, Release 2/2017. [Online] Available from:
https://www.liikennevirasto.fi/documents/20473/236203/Data_Object_Description_2_2017_ENG.pdf [Accessed 10 Jan 2018].
Huotari, T., Antikainen, H., Keistinen, T. and Rusanen J. (2017) Accessibility of tertiary hospitals in Finland: A comparison of
administrative and normative catchment areas. Social Science & Medicine 182: 60-67.
Kotavaara, O., Hakkarainen, T., Huotari, T., Keistinen, T. and Rusanen, J. (2015) Mapping Opportunities for Enhancing Effective-
ness of Health Care System by GIS Based Ac-cessibility Analyses: Locating Core and Support Services within Long Distances in
Northern Finland. In: Proceedings of the 1st ICA European Symposium on Cartography, 2009.
Lankila, T., Kotavaara, O., Antikainen, H., Hakkarainen, T. and Rusanen, J. (2016) Sosiaali- ja terveyspalveluverkon kehityskuva
2025 – Paikkatieto- ja saavutettavuusperusteinen tarkastelu. University of Oulu. [Online] Available from:
http://www.sitra.fi/julkaisu/2016/sosiaali-ja-terveyspalveluverkon-kehityskuva-2025 [Accessed 11 Dec 2017].
McLafferty, S. L. (2003). GIS and health care. Annual review of public health 24(1), 25-42.
Mestre, A. M., and Oliveira, M. D. Barbosa-Póvoa AP (2015) Location–allocation approaches for hospital network planning under
uncertainty. European Journal of Operational Research 240(3):791–806
Miller H and Shaw S-L (2001) Geographic Information Systems for Transportation, Principles and Application. Oxford University
Press
Oulu Public Transport transit data (2017) Oulun joukkoliikenteen liikennöintidata (GTFS) [Online] Available from:
https://data.ouka.fi/data/fi/dataset/oulun-liikenteen-liikennointidata-gtfs [Accessed 2 Jan 2018].
Páez A, Scott DM & Morency C. (2012) Measuring accessibility: positive and normative implementations of various accessibility
indicators. Journal of Transport Geography 25: 141–153
Polo, G., Acosta, C. M., Ferreira, F., & Dias, R. A. (2015) Location-allocation and accessibility models for improving the spatial
planning of public health services. PLoS One 10(3).
Rodrigue, J. P., Comtois, C., and Slack, B. (2016) The geography of transport systems. Taylor & Francis.
University of Oulu / Geography Reserach Unit / Geoinformatics

19. CONCLUSIVE DISCUSSION OPENINGS
1. What are the key public transport modes of the near
future, affecting social and health service accessibility?
/ How the general travel pattern changes?
2. What is the probable and desirable user potential of
public transports?
/ Where? By what conditions, systems, incentives or sanctions?
3. Efficiency and equality - where is the balance in spatial
trade offs in service network coverage?
4. What are the key services needed to be locally
accessible?
University of Oulu / Geography Reserach Unit / Geoinformatics

20. THANKS FOR THE INTEREST!
Contact:
Ossi Kotavaara
Postdoctoral researcher
Geography Research Unit
ossi.kotavaara@oulu.fi
+358 40 727 3618
Low carbon logistics in local social
and health care services