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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
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1Daniel Paez [email protected]
Dr Daniel Paez Universidad de los Andes
2012 ESRI UC
Key Factors Affecting Journey to Work inMelbourne using Geographically Weighted
Regression
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
2/17
Daniel Paez [email protected]
Introduction
This paper presents key factors that affect
journey to work in Melbourne
Research
context
Methodologyand model
Results and
conclusions
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
3/17
Daniel Paez [email protected]
Introduction
This paper presents key factors that affect
journey to work in Melbourne
Research
context
Methodologyand model
Results and
conclusions
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
4/17
Daniel Paez [email protected]
This paper presents results from an empirical
analysis using census data and GIS
Traffic congestion is widely recognised as a growingproblem
focussed on peak periods and commuting travel by cars effectiveness of measures depends greatly on ourunderstanding of the factors driving car use
This paper presents the results of an empirical analysis using geographic information systems Census data
The focus of the work is car commuting in Melbourne,Australia.
Analysis part of a wider study of car dependence
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
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Daniel Paez [email protected]
Introduction
This paper presents key factors that affect
journey to work in Melbourne
Research
context
Methodologyand model
Results and
conclusions
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
6/17
Daniel Paez [email protected]
There are some common factors in previous research butlocal conditions change their relative importance
Based on a literature review, factors affecting carchoice for JTW are:
car ownership access to transit distance from the CBD CBD employee share To some extent urban residential and employment density
However there is some variation in specific factors degree to which each is of influence local conditions
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
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Daniel Paez [email protected]
A geographic weighted regression explored
the spatial pattern of regression results
Characteristics This technique allows local as opposed to global models of
relationships to be measured and mapped
Main assumption is that spatial phenomena (e.g. cardependency) will vary across a landscape
This tool generates a separate regression equation for everyfeature analyzed in a sample dataset as a means to address
spatial variation
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
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Daniel Paez [email protected]
Geographic weighted regression has both
advantages and disadvantages
Advantages Regression-based models largely ignore spatial variation, much
to the detriment of spatially varying relationships (e.g. proximity to
train stations)
In most cases improves the certainty of the model Disadvantages
Not commonly used and, therefore, results are complex toanalyze
The spatial distribution of coefficients is developed finding smoothpatterns and they should only be used to identify trends and notpredictions
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
9/17
Daniel Paez [email protected]
Introduction
This paper presents key factors that affect
journey to work in Melbourne
Research
context
Methodologyand model
Results and
conclusions
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
10/17
Daniel Paez [email protected]
A four step methodology was used in this
research
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
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Daniel Paez [email protected]
Eleven variables were explored as factors
affecting JTW in Melbourne
Variable DescriptionPublic Transport Supply
Estimation of the level of service of Public Transport in each CCD
based on access, frequency and spam
Public Transport Ranking Relative ranking of each CCD in relation to PT supply
Residential Density Total number of people divided by area
Distance to Business Zone Linear distance to the closest business 1 planning zone
Distance to Rail Station Linear distance to the closest metropolitan train station
Distance to MelbourneCBD
Total distance using the shortest path in the road network
Distance to Local ActivityCentre
Linear distance to the closest activity centre (CAD, PAC or MAC)
Distance to Arterial Road Linear distance to the closest arterial road
Distance to Highway Linear distance to the closest Highway
Provision of Roads Level of service in relation to roads within the CCD
Provision of Cycling Ratio between the Cycling network in the area and total numberof persons
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
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Daniel Paez [email protected]
Introduction
This paper presents key factors that affect
journey to work in Melbourne
Research
context
Methodologyand model
Results and
conclusions
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
13/17
Daniel Paez [email protected]
The standard regression model produced a
limited result
SUMMARY OUTPUT
Regression StatisticsMultiple R 0.756081099R Square 0.571658628Adjusted R Square 0.570957196Standard Error 0.083805347Observations 5506
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
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Daniel Paez [email protected]
The SWR improved fit of the model
significantly
NAME VALUE
Bandwidth 0.27319257992
ResidualSquares 22.34178023090
Effec>veNumber 58.97956002890Sigma 0.06404413809
AICc -14590.88225500000
R2 0.75207654510
R2Adjusted 0.74943758074
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
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Daniel Paez [email protected]
In provision of public transport appears to be
more relevant in south west areas
Geographic distribution of the regression coefficient for PT supply
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
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Daniel Paez [email protected]
Proximity to the CBD is relevant to car use across Melbournewith the highest impact in the Inner and Northern areas
Geographic distribution of the regression coefficient for Distance to CBD
VehicleUsageforJTW
Distance to CBD
5 10
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7/31/2019 Daniel Paez - Esri Uc 2012 - Swr
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Daniel Paez [email protected]
High residential densities have an impact on reduced caruse in growth areas and in the inner city
Geographic distribution of the regression coefficient for population density
Importanceofpopulationd
ensity
inrelationtodrivingchoice
Distance to CBD