A GIS Based Investigation of Spatial Accessibility to...

A GIS Based Investigation of Spatial Accessibility to Healthcare Facilities within an Urban Fringe Area of Melbourne, Australia

Gang-Jun Liu Salahuddin Ahmad Benno Engels

School of Mathematical and Geospatial Sciences

RMIT University, Melbourne, Australia

29th August 2013

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Outline

1. Objectives and Methodology

2. Study Area, Data and Results

3. Discussions, Conclusions, and Next Move

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Objectives • Uncover hidden patterns of spatial variations in accessibility to healthcare facilities

• Locate spatial clusters of disadvantaged locations and communities

• Produce useful evidence for improving–understanding / explanation–policy making and evaluation–spatial accessibility, wellbeing and quality of life

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Methodology Procedures1. Locating

– people– healthcare facilities– transportation networks

2. Relating– them at fine spatial resolution

3. Measuring– spatial concentration of people– levels of services / attractiveness of healthcare facilities– travel impedance (distance, time, etc.) via transportation network – spatial accessibility from residential locations to locations of healthcare facilities

4. Identifying spatial clusters of disadvantaged locations that have– High concentration of people (need), and– Low level of healthcare service provision (hence, poor spatial access to healthcare

facilities)using spatial statistical indicators and spatial overlay operations

5. Leading to better understanding / explanation; evidence-based policy making and evaluation; and effective strategies for spatial optimisation.

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Methodology Spatial Accessibility• Perspectives

–Interactions among demand, provision and transport

–Utility and constraint imposed by the interplay among space, time, and thematic domain

–Potential vs realised , objective vs subjective, …

• Measures –Opportunity based measures–Ratio based measures–Impedance based measures–Gravity based measures–Utility based measures–Constraint based measures

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Origin (Demand/Population)

Destination(Supply/Opportunity/ 

Service/Facility)

Trip

Transportation Network

Transport Mode 

Travel Route

User-centredAccessibility

(WHO)

Points in Time(WHEN)

Locations in Space (WHERE)

Opportunities / facilities(WHAT)

Study Area

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Area: 1,281 km2

Population: 45,552Dwelling: 21,075

(ABS 2006)

Data

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Source Type Description FormatABS Spatial Mesh Block boundary Polygon

Census Collection District Boundary

Polygon

Attribute Census Collection District level population census of 2006

Excel Table

VicMap Spatial Address points Point Admin boundaries (Shire and locality boundary)

Polygon

Road network Line Rail network, train stations Line, Point

Metlink Spatial Bus routes, bus stops Line, Point Attribute Time tables PDF

DHS Address and attribute

Health care services locations and attribute

Excel Table

Yellow Pages Address and attribute

Health care services locations and attribute

CSV

ResultsLocating and relating 8 categories of land use, MB level population and dwellings, Locations of health care facilities, and

Transport infrastructure (links / routes and stops /

stations).

Measuring and identifying Travel distances and travel times (max, mean, SD) % of population in specific travel distance / time zones

Spatial Clusters of disadvantaged locations /

communities

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Land Use Population

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Residential Addresses

Healthcare Facilities

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Transport Infrastructure

Car Ownership> 30% of dwellings have one or no car in 2006

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Trav

el D

ista

nce

Trav

el T

ime

Pharmacy GP / Surgeon Dentist)(

h

nkk

kij v

lT

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Health care facilities

Maximum(m)

Average (m)

SD (m)

Pharmacy 24,478.3 3,228.6 3,348.9

GP/Surgeon 26,996.1 5,848.2 4,861.7

Dentist 31,393.4 6,187.6 6,275.6

Summary statistics of travel (driving) distance (ABOVE) and travel (driving) time (BELOW) from MB centroids to nearest health care facilities

Health care facilities

Maximum (min)

Average (min)

SD (min)

Pharmacy 31.24 3.91 3.93

GP/Surgeon 34.40 6.45 5.36

Dentists 38.78 6.57 5.86

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Pharmacy GP / Surgeon Dentist

Trav

el D

ista

nce

Trav

el T

ime

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Summary statistics Pharmacies GP/Surgeons clinics Dentists Ai

Minimum 0 0 0 0.0014Maximum 1 1 1 0.9978Mean 0.1314 0.2159 0.1965 0.1717SD 0. 136773 0.1801 0.1998 0.1366jj

jij

ij dd

ddD

minmax

min

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0.1 and 4.0 ,5.0

31

j

ijjji

wwhere

DwA

Identifying Spatial Clusters of Disadvantaged Locations / Communities

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A: 9% (~ 4000 persons)

Identifying Spatial Clusters of Disadvantaged Locations / Communities

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B: 4.5% (~ 2000 persons)

Identifying Spatial Clusters of Disadvantaged Locations / Communities

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C: 5.5% (~ 2500 persons)

Identifying Spatial Clusters of Disadvantaged Locations / Communities

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A B C

Identifying Spatial Clusters of Disadvantaged Locations / Communities

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A B C Scenario 1 Scenario 2

1 1 1 1 11 1 2 3 11 1 3 3 11 2 1 3 11 2 2 3 21 2 3 3 31 3 1 3 11 3 2 3 31 3 3 3 32 1 1 3 32 1 2 3 22 1 3 3 32 2 1 3 22 2 2 2 22 2 3 3 22 3 1 3 22 3 2 3 22 3 3 3 33 1 1 3 13 1 2 3 33 1 3 3 33 2 1 3 33 2 2 3 13 2 3 3 33 3 1 3 33 3 2 3 33 3 3 3 3

Identifying Spatial Clusters of Disadvantaged Locations / Communities

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~ 2500 persons~ 2000 persons

Discussions: Measuring Travel Impedance

• Travel time–Speed limits (infrastructure vs agent)–Time of the day / week / year (seasonality)–Direction–Weather condition

• Travel distance –Straight line distance–Road network distance–Impact of multimodal network connectivity, distance decay, path slope and fitness level of people

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Distance Measure Distance (m)L0 - Straight Line 1690.4L1 – Network, Flat 1890.4L2 – Network, Slope 1902.9L6 – Network, Slope, Strong 2969.1L9 – Network Slope, Average 3502.2L12 – Network, Slope, Weak 4035.3

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Travel Distance to Nearest Maternal and Child Health Centre for Households with Children Under 5 years of Age

Conclusions 1. Spatially dispersed population and uneven distribution / provision of

healthcare facilities across geographical space leads to location advantages and disadvantages by virtue of where people live

2. Location-Enabled Approaches underpinned by spatial datasets and GIS-based spatial analytical procedures have been used for

– the mapping of people, healthcare facilities and transport system– the measurement of population concentration, network based travel

impedances between population centres and sites of healthcare provision, and ease of access to these essential services and facilities by local communities

– the identification of spatial clusters of disadvantaged locations / communities

3. With proper modifications of the datasets and analytical settings, the methodology developed in this study would be applicable to different population groups, practical issues, time periods, and geographical (either urban or rural) settings where spatial accessibility is an important issue.

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Next Move1. Better understanding / representation of the

• need and preference of population groups,• capacity / attractiveness of health care facilities,• multimodal transportation system, including

– connectivity of links / routes, and – capacity / attractiveness of stops / stations;

2. Better measurement of accessibility, by• using improved data,• considering impacts of ICT / ITS, and• developing more appropriate formulation of

accessibility measures.

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Questions ?