Daniel Paez - Esri Uc 2012 - Swr

7/31/2019 Daniel Paez - Esri Uc 2012 - Swr

1/17

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


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


3/17


Introduction



Research

context


Results and

conclusions


4/17


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


5/17


Introduction



Research

context


Results and

conclusions


6/17


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


7/17


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


8/17


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


9/17


Introduction



Research

context


Results and

conclusions


10/17


A four step methodology was used in this

research


11/17


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


12/17


Introduction



Research

context


Results and

conclusions


13/17


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


14/17


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


15/17


In provision of public transport appears to be

more relevant in south west areas

Geographic distribution of the regression coefficient for PT supply


16/17


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


17/17


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

Documents

Daniel Paez - Esri Uc 2012 - Swr