The role of neighbourhoods accessibility in residential mobility

Cities 87 (2019) 1–9

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The role of neighbourhoods accessibility in residential mobility ⁎

T

Tayebeh Saghapour , Sara Moridpour Civil and infrastructure Engineering Discipline, School of Engineering, RMIT University, Melbourne, Australia

A R T I C LE I N FO

A B S T R A C T

Keywords: Accessibility measures Residential location Built environment OLR models

Household decisions to move from or stay at a current location may be based on a great number of variables. There has been substantial discussion among planners about the effect of the built environment in the choice of residential location. However, there is limited research on the role of non-motorised accessibility on residential location. Households may base their decision to move from or stay at a current location on the neighbourhood's accessibility. The public transport accessibility, walkability and bikeability of a neighbourhood may affect residents' decisions to stay or move from their current location. The focus of this paper is on modelling and comparing the influence of non-motorised accessibility measures on the number of years that households stay at their current location. The paper addresses this issue by employing three non-motorised accessibility measures in separate ordered logistic regression (OLR) models. Focusing on metropolitan Melbourne, Australia, the Victorian Integrated Survey of Travel and Activity (VISTA, 2012) was adopted to model years of residency incorporating socio-economic characteristics, built environment features and accessibility measures. Key findings indicate that non-motorised accessibility has statistically-significant impacts on the number of years that residents live at their current address. Furthermore, of the accessibility measures, access to public transport has the greatest impact.

1. Introduction Housing is the one of the most important assets of an individual household. Residential decisions can be divided between the decision to move residential location and the intention to move (Kim, Pagliara, & Preston, 2005). The change of spatial structure in urban areas is largely known to be an outcome of residential mobility and residential location choice (Knox & Pinch, 2014; Wu, 2004). Household choice of residential location is a function of a wide range of housing and location attributes, and the relative importance of these attributes varies by different types of household. According to Kim et al. (2005), there are two main methods to investigate the factors that influence household mobility behaviour and location selection. One is to examine the attractiveness of different places for movers and then identify the characteristics that correlate with levels of attractiveness. This requires aggregate level data (Bruch & Mare, 2006; Weisbrod, Lerman, & BenAkiva, 1980). The other is a disaggregated approach based on household or individual data. This approach is based on a survey of residents in order to determine the reasons why they choose to stay in one place or move to another (Champion & Fisher, 2004). The literature on residential mobility and built environment factors is extensive across multiple related disciplines. However, there is still no significant research that shows the role of accessibility as an

⁎

important factor for staying in a current housing location. Hence, this research aims to highlight the different factors that influence the decision to stay in a certain residential location in Australia. Specifically, it underscores the role of non-motorised accessibility on the number of years that residents stay at the same address, based on the case of Melbourne in Australia. This research also compares accessibility measurements in terms of their impact on residential mobility. For this purpose, three non-motorised accessibility indexes are employed in three separate ordered logistic regression (OLR) models to examine if accessibility affects the number of years that residents live at their current location. 1.1. Residential mobility in Australia The Australian Census of Population and Housing (ABS, 2016) is the main source of statistics on internal migration in Australia, both for local areas and for sub-populations. The term “internal migration” includes estimates of internal migration down to statistical areas and considers a change in address from one or five years prior to the Census. The latest Census shows a modest increase in Australian internal migration, with all changes of address increasing between 2011 and 2016. This increase was mainly limited to local moves, specifically within major cities. On average, moves between statistical areas in capital

Corresponding author. E-mail address: [email protected] (T. Saghapour).

https://doi.org/10.1016/j.cities.2018.12.022 Received 8 August 2018; Received in revised form 13 November 2018; Accepted 20 December 2018 0264-2751/ © 2018 Elsevier Ltd. All rights reserved.

Cities 87 (2019) 1–9

T. Saghapour, S. Moridpour

researchers (e.g. Cao, Mokhtarian, & Handy, 2009) have investigated whether observed patterns of travel behaviour affect residential location and the built environment. This concept is known as residential self-selection, which means that people choose their residential location on the basis of their travel needs and preferences (Humphreys & Ahern, 2017). On the other hand, there is substantial research on the influence of land-use factors on residential location (Chatman, 2014; de Abreu e Silva, 2014). Cao, Handy, and Mokhtarian (2006), found that apart from land-use factors (e.g. mixed land uses and interconnected street networks), the availability of shops within walking distance affects residential location. Some other studies have considered the association between accessibility and residential location choice. Nurlaela and Curtis (2012) conducted research on residential location choice and developed a methodological framework to reveal the intervening factors within the relationship between the choice of residential location and mode of travel. Thériault, des Rosiers, and Joerin (2005) claimed that accessibility is one the main determinants which affects property values, and property value plays a positive interrelationship in housing decisions. Several studies have also been conducted on walkability in neighbourhoods and residential locations (Cao et al., 2006; Duncan et al., 2012; Pinjari, Pendyala, Bhat, & Waddell, 2011). These studies confirm the importance of environmental factors in the walkability of residential locations. In research by Ibrahim (2017), access to public transport was found to be an influential factor affecting choice of residential location. Although there is some research on the influence of accessibility on residential location choice, there remain gaps in the literature on the influence of accessibility to non-motorised modes of transport and the tendency to stay in a neighbourhood. In other words, there is no significant research on the importance of accessibility and residential mobility. Hence, this study focuses on investigating the association between non-motorised accessibility measures within neighbourhoods and residents' intention to stay in an area. Moreover, the current study investigates the importance of walking, cycling and public transport accessibility measures on residential mobility. Table 1 summarizes the dominant variables considered to be influential factors on residential location in previous research. As the table shows, very few studies have considered non-motorised accessibility as influential factors on residential location. In addressing this issue, this paper uses built environmental measures, including a land use mix index, connectivity, population density, roadway measures and three non-motorised accessibility measures along with socioeconomic characteristics to determine the impact of accessibility on residential location. The next section presents the methods of the study, and describes the dataset, study area and explanatory variables. This is followed by the analysis and results of the modelling (Section 3). Thereafter, in Section 4, the results of the analysis are discussed, and the conclusions are described.

cities increased by 5.1% from 2011 to 2016. The largest growth was in Greater Perth, where local moves increased by 14%. Local moves were reported to be 10% and 9.5% in Sydney and Melbourne, respectively. The increase in local moves may be related to the declining levels of home ownership, with renters generally being more mobile than owners. Accordingly, there has been a construction boom in the inner ring of Australian capital cities, increasing residential mobility as people are encouraged to live in newer dwellings closer to city centres. As stated by Champion, Cooke, and Shuttleworth (2017), socioeconomic factors such as age, household income, housing affordability, and the maturation of the Australian space economy affect internal migration. A range of other characteristics also affect the likelihood of residential mobility in Australia (ABS, 2016). These include economic characteristics such as educational level, employment status, type of employment, social factors such as indigenous status, country of birth, and housing tenure. Records from the census 2016 revealed that individuals with Certificate I & II level qualifications in the first place and people with postgraduate degrees had higher levels of mobility. Although employment is commonly known as a reason for internal migration, unemployed Australians or those not in the labour force are much more likely to move than employed Australians, whilst among employed people, the higher level of internal migration was for employees in public administration and safety. Records also indicated that Aboriginal and Torres Strait Islander people have a higher rate of mobility than non-Indigenous Australians, and people born overseas (18%) are more likely to move internally than the Australian-born (14%). 1.2. Residential mobility and neighbourhood attributes Households' decisions about residential area depend on various factors, including property values, accessibility, socio-economic characteristics, dwelling type, dwelling ownership and neighbourhood attributes (Nurlaela & Curtis, 2012; Vega & Reynolds-Feighan, 2009). According to Lee and Waddell (2010), residential mobility and location choice are important aspects of integrated transportation and land use planning. These decision processes have been examined and modelled separately to a great extent. In transportation-land use modelling, there have been contributions on residential mobility (Eluru, Sener, Bhat, Pendyala, & Axhausen, 2009; Habib & Kockelman, 2008) and a considerable list of current research on residential location (Chen, Chen, & Timmermans, 2008; Guo & Bhat, 2007; Lee & Waddell, 2010; Lee, Waddell, Wang, & Pendyala, 2009). During the last decades, a large number of studies have been conducted on residential mobility, investigating different factors such as socioeconomics, and geographic and built environment contexts (Knox & Pinch, 2014; Lee & Waddell, 2010). A study by Lee and Waddell (2010) indicated that among socioeconomic characteristics, there are three close connection between mobility rates and life cycle of an individual, housing size and dwelling ownership and changes in education and work opportunities (Li & Wu, 2004; Lee & Waddell, 2010; Clark & Huang, 2003; Prillwitz, Harms, & Lanzendorf, 2006). Rania, Allah, and Khalil (2014) also state that income, life events, family size, the level of education, household characteristics, marital status, and stage of the life cycle are significant factors affecting housing relocation (Kerstens & Pojani, 2018; Willing & Pojani, 2017). Moreover, housing tenancy maybe another factor, and Coulson and Fisher (2009) found that homeowners tend to move less than those who rent. In other studies by Van Acker, Witlox, and Van Wee (2007) and van de Coevering and Schwanen (2006), the researchers argued that demographic and household characteristics had a stronger influence on residential location choice than built environment factors. Kim et al. (2005) claimed that transport-related attributes had significant impacts on residential location choice. Based on their estimation results, individuals prefer residential locations with a combination of shorter commuting time, lower transport costs, lower density and higher quality of schools. Some

2. Methodology The current study used an access level measure obtained from nonmotorised accessibility indices (Saghapour, Moridpour, & Thompson, 2016c; Saghapour, Moridpour, & Thompson, 2017b; Saghapour, Moridpour, & Thompson, 2017c; Saghapour, Moridpour, & Thompson, 2018) to examine residential location choice. For this purpose, the number of years that a household has lived at the same address (YL) was defined as a dependent variable in three separate OLR models. Fig. 1 shows the conceptual framework of the study. The following sections describe the data sources and study area and the explanatory variables considered. 2.1. Datasets and study area Socio-economic characteristics were obtained from the Victorian Integrated Survey of Travel and Activity (VISTA, 2012). This cross2

Kim et al., 2005 Wu, 2004 Weisbrod et al., 1980 Champion & Fisher, 2004 Vega & ReynoldsFeighan, 2009 Nurlaela & Curtis, 2012 Habib & Kockelman, 2008 Eluru et al., 2009 Chen et al., 2008 Guo & Bhat, 2007 Lee et al., 2009 Lee & Waddell, 2010 Clark & Huang, 2003 Prillwitz et al., 2006 Rania et al., 2014 Kerstens & Pojani, 2018 Willing & Pojani, 2017 Coulson & Fisher, 2009 Van Acker et al., 2007 van de Coevering and Schwanen, 2006 Kim et al., 2005 Humphreys & Ahern, 2017 de Abreu e Silva, 2014 Cao et al., 2006 Thériault et al., 2005 Pinjari et al., 2011 Ibrahim, 2017

Author(s),year

Property value

+ − −

−

−

+

+

− − −

+ −

− − −

−

−

−

−

− − −

− −

+ + +

+

+

+

+

+ + +

+ +

3 + − −

−

− −

−

−

−

−

+ −

−

− −

−

−

−

+ +

+

−

−

−

− −

−

− −

−

−

+

+ +

+

+

+

+

+ +

+

+ +

+

+

+

− −

+

− −

−

− − −

− +

+

−

+

+

− +

−

+

− +

+ − +

+

−

+

+

− − −

Dwelling Ownership

− −

−

−

−

−

−

−

−

+ −

− − −

+

−

−

+

− − +

Rent/ mortgage

+

Socio economics variables

Life cycle events

Table 1 Summary of dominant factors affecting residential location.

+

+

− −

−

+ +

+

−

−

+

+ −

−

−

− −

− + −

+

−

−

−

− + −

Dwelling size/type

−

+

− +

−

+ −

+

+

−

+

− +

−

−

+ −

− − +

−

−

−

−

− − −

Population Density

−

−

− −

−

− −

+

−

−

−

− −

−

−

− −

− − +

−

−

−

−

− − −

Employment Density

−

+

− +

+

− +

−

+

−

−

− −

−

−

− −

− + +

−

−

−

−

− − −

Mixed use development

+

+

+ +

+

+ +

−

+

−

+

− +

−

−

− +

− + +

−

−

+

−

+ − −

Travel cost (distance/ time)

−

−

+ −

+

− −

−

−

−

−

− −

−

−

− −

− − −

−

−

−

−

− − −

Walkability

−

−

− −

+

− −

−

−

−

−

− −

−

−

− −

− − −

−

−

−

−

− − −

Bikeability

+

−

− −

−

− +

−

+

−

+

− +

−

−

− −

− − −

−

+

+

−

− − −

Public transport accessibility

−

−

+ −

−

− −

+

−

−

−

− −

−

−

− −

− − −

−

−

−

−

− + −

Infrastructure developments

+

−

− −

− −

−

−

−

+

− +

−

−

− −

+ − −

−

−

−

−

− − −

Vicinity to friends and family

(continued on next page)

−

−

− −

−

− −

−

−

−

−

− −

−

−

− −

− − −

−

−

−

−

− − +

Level of safety in neighbourhoods

T. Saghapour, S. Moridpour

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− −

sectional survey was conducted from 2012 to 2013 and covers the Melbourne region. Data collected include demographics and trip information from randomly-selected residential properties. The data used in this study refers to a total of 8994 households, comprising 22,934 individuals. Among the respondents this study excluded those households who did not own a dwelling. Hence, the data selected for analysis include 3819 households, including 8562 individuals within the Melbourne region. 2.1.1. Spatial data The spatial data used in this study was obtained from the Australian Bureau of Statistics (ABS). This dataset has the total usual resident population and dwellings from the 2011 Census of Population and Housing for mesh blocks and all other statistical areas. According to the Australia Bureau of Statistics (ABS, 2016), the Melbourne region is divided into 53,074 mesh blocks, 9510 statistical areas level 1 (SA1s), 277 statistical areas level 2 (SA2s) and 31 local government areas (LGAs). Fig. 2 presents the statistical geography areas of the Melbourne region. Mesh blocks are the smallest geographical units and all other statistical areas are built up from, or approximated by, whole mesh blocks. In this study, SA1s were chosen as the geographical scale for the analysis and calculation of the built environment factors. The reason for choosing SA1s was that SA1s have the closest conformity to the definition of neighbourhood with an average area and population of roughly one km2 and 414, respectively.

+

2.2. Explanatory variables Two groups of explanatory variables, socioeconomic characteristics and built environment measures, were included in the analysis. Age, sex, number of cars in household, possession of a driving licence, type of dwelling, work arrangement, size of household, and personal income were employed as socioeconomic variables (Cao et al., 2009; Ewing & Cervero, 2010; Jun, Kim, Kwon, & Jeong, 2013; Kim, Park, & Lee, 2014; Manaugh, Miranda-Moreno, & El-Geneidy, 2010; Nilsson & Küller, 2000; Winters, Brauer, Setton, & Teschke, 2010; Zondag & Pieters, 2005). On the other hand, mixed-use development, population density, roadway measure, connectivity and accessibility measures were considered to be built environment factors. Explanatory variables were adopted from a study by Saghapour (2017). Using geographic information system (GIS) techniques, all built environment measures were calculated for 9510 Melbourne SA1s. The following sections briefly explain the above variables. 2.2.1. Entropy index (EI) The EI a common index used for measuring mixed-use development. This index is computed when the numerator is normalized by the natural logarithm of the number of land-use categories. Eq. (1) presents one of the most common formulas for measuring mixed-used development within geographical units (Cerin, Leslie, Toit, Owen, & Frank, 2007; Duncan et al., 2010; Kim et al., 2014; Nilsson & Küller, 2000; Song, Merlin, & Rodriguez, 2013):

−

Note: +: included variable, −: not included variable.

− + Duncan et al., 2010

−

−

−

−

−

−

−

−

−

−

Vicinity to friends and family Walkability Population Density Rent/ mortgage Socio economics variables Author(s),year

Table 1 (continued)

Life cycle events

Property value

Dwelling Ownership

Dwelling size/type

Employment Density

Mixed use development

Travel cost (distance/ time)

Bikeability

Public transport accessibility

Infrastructure developments

Level of safety in neighbourhoods

T. Saghapour, S. Moridpour

EIi = −

J Pj∙ln Pj ⎞ ⎛ ∑ ⎜ lnJ ⎟ ⎝ j=1 ⎠

(1)

where, EIi indicates the entropy index within SA1s, Pj represents the proportion of a type of land use j and J is the number of land-use categories. This study computed the index for six groups of land uses, including residential, industrial, commercial, community services, transport and infrastructure and recreation centres. These categories were adopted from the ten main categories of uses defined by the Australian Valuation Property Classification Codes (AVPCCs) (MorseMcNabb, 2011). The value of this index varies from 0 to 1, with 1 indicating a perfect balance among different types of land use and 0 showing full homogeneity. 4

Cities 87 (2019) 1–9

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Fig. 1. Conceptual framework of the study.

Fig. 2. Statistical areas in Melbourne Region.

2.2.3. Roadway measure (RWM) RWM measures how far the network spreads over an area, which is defined as SA1 in this study. It is calculated by the total length of roadway divided by the total area, while the distance is normalized by a unit area of 100m2 (Kim et al., 2014; Lee, Nam, & Lee, 2014).

2.2.2. Population density (PNDY) Population density is a simple concept indicating the number of residents in a given area. PNDY is one of the most important measurements indicating the distribution of population, and is widely used in urban and transport research (Chakhtoura & Pojani, 2016; Cole, Burke, Leslie, Donald, & Owen, 2010; Ewing & Cervero, 2010; Ewing, Meakins, Hamidi, & Nelson, 2014; Manaugh & Kreider, 2013).

2.2.4. Connectivity (CNTY) The connectivity measure also has a simple calculation method. It measures the ratio of the number of intersections divided by the total 5

Cities 87 (2019) 1–9

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2.2.7. Public Transport Accessibility Index (PTAI) Public transport accessibility is calculated using the Public Transport Accessibility Index (PTAI) (Saghapour et al., 2018; Saghapour, Moridpour, & Thompson, 2016a). The PTAI measures the level of access to public transport within Melbourne's 9510 SA1s. The PTAI includes the service frequency of public transport modes as well as the population density ratio in walking catchments, as shown in Eq. (4):

number of intersections within a certain area (Kim et al., 2014; Knaap, Song, & Nedovic-Budic, 2007; Song & Knaap, 2004). CNTY values within SA1s were adopted from the Australian Urban Research Infrastructure Network (AURIN) (Sinnott, Galang, Tomko, & Stimson, 2011). AURIN provides a web-based environment that calculates connectivity for different statistical subdivisions within the Melbourne region. 2.2.5. Walking Access Index (WAI) The WAI measures walkability within Melbourne's 9510 SA1s (Saghapour et al., 2018; Saghapour, Moridpour, & Thompson, 2017a). This measure considers the walking distances to available destinations as one of the main barriers to making walking trips. Walking distances are calculated as the average distance from the weighed centroid of a SA1 to all available points of interest (POIs) or destinations within acceptable walking distances. POIs are grouped into six categories of destinations, including primary and secondary schools, tertiary institutions, child care centres, medical centres, retail and recreation centres, and community services and libraries. Acceptable walking distances are defined as median desirable and maximum desirable travel distances. The POI categories and the travel distance definitions are adopted from Australian and New Zealand Road Transport and Traffic Authorities, (AUSTROADS, 2011). Eq. (2) presents the formula used to calculate the WAI for SA1s. A DM j − Dij ⎞ D ⎟ Dj ⎠ ⎝

6

WAISA1i =

∑ Ni × ⎛⎜ j=1

if D Bij = 0; 3

PTAISA1 =

4 j=1

⎛ X = Dij , N ≠ 0⎞⎟ ij ij Bli ⎠ ⎝

⎜

⎝

D Bij ⎞ ⎟ ∗ WEFSA1i DSA1i ⎠

if D Bij ≠ 0; 3

PTAISA1 =

I

D Bij ⎞ ⎟ ∗ WEFSA1i ⎝ SA1i ⎠

∑ ∑ ⎛D ⎜

j=1 i=1

where, PTAISA1 denotes the public transport accessibility index for a given SA1 and DBij is the population density of buffer i for public transport mode j, DSA1 is the population density of the SA1, and WEFSA1 is the weighted equivalent frequency calculated for the corresponding SA1. For SA1s with no population, PTAI is equal to the weighted equivalent frequency of public transport modes within that area. The calculation process has two steps, one is calculating walking catchments for public transport stops/stations and the other is to compute the service frequency of public transport modes. More details and explanations are provided in studies by Saghapour et al. (Saghapour et al., 2016c; Saghapour, Moridpour, & Thompson, 2016b). A higher value of the PTAI indicates a higher level of accessibility. A value of 0 indicates that there is either no accessibility or no population in a SA1. The PTAI ranges from 0 to 115.68 with an average value of 10.76.

(2)

2.3. Ordered logistic regression (OLR) model Ordered logistic regression (OLR), also known as ordinal regression, is a type of regression analysis used for predicting an ordinal variable. In this sort of regression, the dependant variable has an arbitrary scale where only the relative ordering between different values is significant. OLR can be performed using a generalized linear model (GLM) that can be applied on a coefficient vector and a set of thresholds to a dataset. In other words, OLR models estimate a regression coefficient over the levels of the response variable. Estimates from the model are denoted as ordered log-odds regression coefficients. Interpretation of the ordered logit coefficients is that for a one-unit increase in the predictor, the response variable level is expected to change by its respective regression coefficient in the ordered log-odds scale, while the other variables in the model are held constant. Interpretations of ordered logit estimates are not dependent on auxiliary parameters. Secondary parameters are used to differentiate the adjacent levels of the response variable. The odds ratio (OR) which is estimated in this model can be obtained by using the exponential function and the coefficient estimate (i.e. eCoef.). To interpret this, persons who are in groups greater than k are compared to those who are in groups less than or equal to k, where k is the number of response variable levels (Andren, Flood, Hansen, Popa, & Tasiran, 1999). A typical model for cumulative logits is shown in Eq. (5):

2.2.6. Cycling Accessibility Index (CAI) The CAI measures bikeability within Melbourne's 9510 SA1s. This index has been built up from cycling catchments as well as travel impedances between origins and destinations. Origins are considered as the weighted centroids of SA1s and four distinct categories of activities are defined as destination groups. Destination categories include education centres, health and care facilities, retail and recreation centres and community services. CAI is formulated as shown in Eq. (3).

∑ e−Xij

I

∑ ∑ ⎛1 + j=1 i=1

where, WAISA1 is the Walking Access Index for each SA1, Ni is the number of destinations (POIs) available within the acceptable walking distance for SA1i, DjM is the maximum walking distance to destination type j which is defined as a value at which the majority of people would find it unfeasible to regularly travel and they may have to relocate their residence closer to the destination or find a less suitable destination that is closer. DjD denotes the desirable walking distance to destination type j, which shows a value that satisfies half of travellers, and DijA represents the average walking distances from the weighted centroid of SA1i to all activities within destination type j. A higher value of WAI indicates a higher level of accessibility. A value of 0 indicates no accessibility in terms of the availability of destinations within the acceptable distance (cut-off values). The WAI ranges from 0 to 222.43 with an average value of 24.08.

CAIi = ARi +

(4)

⎜

(3)

where, CAIi is the Cycling Accessibility Index for each SA1, ARi is the ratio of the combined cycling catchment areas in SA1i to the total area of SA1i, Xij is the travel impedance, which is the ratio of average travel distance between origin i and destination type j to the total bicycle length in the corresponding SA1. For areas with no bicycle network, the CAI is equal to ARi. The logic behind this is that in areas with no bicycle lane, cyclists may share the road with other modes. In addition, for areas with no destinations (or no points of interest) within an acceptable distance or travel time, the value is zero. In other words, if Nij = 0, then Xij and Dij are undefined. More details and illustrations of the CAI are provided in studies by Saghapour et al. (2017c, 2018). The CAI ranges from 0 to 44.7 with an average value of 2.98.

logit[P (Y ≤ j )] = α j + β1 X1 + β2 X2 + …+βn Xn = α j + β´ X

(5)

where, j = 1, …, c − 1; c is the total number of categories, x1, x2, …, xn are n explanatory variables, and β1, β2, …, βn are the corresponding coefficients. 6

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Table 2 Frequency of number of years residents have lived in their current location. YL

Categories

1 2 3 4 5 Total

<7 7–12 13–20 21–29 > 30

Frequency

Percentage

Cumulative percentage

1688 1556 1862 1507 1949 8562

19.7 18.2 21.7 17.6 22.8 100.0

19.7 37.9 59.6 77.2 100.0

Table 4 Descriptive statistics of continuous variables.

n = 8562.

3. Results Explanatory variables were employed in three separate OLR models to examine the impact of accessibility on the number of years residents lived at their current address. In each model one of the non-motorised accessibility measures, PTAI, WAI and CAI, were included. While accessibility measures were correlated with each other, they were not included in the same model. The following sections explain the OLR models as well as the variables used. Categories of the number of years which residents lived (YL) at their current address were defined as an ordered dependent variable. Having an ordered dependent variable, OLR models were used to explore the effects of socioeconomic characteristics as well as access indices. Table 2 shows the frequency of YL categories. It should be noted that the sample included only households who owned a dwelling. Table 3 describes the variables used in the analysis. As the table shows, variables were defined as two groups of socioeconomic characteristics and built environment factors. Table 4 presents the descriptive statistics for the continuous variables used in the analyses. These statistics were calculated for the 8562 individuals. As explained previously, this number includes only respondents who own a dwelling. According to Table 3, the average age of respondents was about 51. The average household size shows that the respondents were mostly from households with about three residents. The average number of years lived at the address was 19. Table 5 presents the outcomes of the OLR models for YL. Regarding the socioeconomic factors, the results show that household size, income and having a driving licence are negatively associated with residents staying at their current address. Owning a separate house is strongly

Socioeconomic characteristics Age Sex1 Cars PINC LNC1 HHS DWT2 YL WT3 Built environment measurements CAI WAI PTAI RWM CNTY EI PNDY

Mean

S.D.

Min

Max

Age Cars HHS YL CAI WAI PTAI RWM CNTY EI PNDY

51.27 1.59 2.86 19.23 2.72 23.08 11.18 1.34 3.25 0.39 2931.55

22.64 1.00 1.34 13.75 2.87 16.61 32.42 0.71 6.50 0.13 2426.50

0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

98.00 6.00 6.00 88.00 32.75 212.02 115.68 5.57 92.06 0.87 158,817.12

associated with YL. In other words, owning a separate house strongly affects residents' decision to live in their current neighbourhood. The results indicate that there is a significant association between built environment factors and years lived at the same address. However, compared to socioeconomic variables, land use mix, connectivity, roadway measure and population density have small effects on residential mobility. In contrast, non-motorised accessibility shows a greater impact on YL. The odds ratio (OR) of PTAI denotes that a oneunit increase in PTAI makes it 1.23 times more likely that residents stay in their current location. This number is 1.19 for WAI and 1.34 for CAI. 4. Discussion and conclusions The aim of this study was to investigate the influence of non-motorised accessibility on residential location choice. For this purpose, three accessibility measures, a walking access index, a cycling access index and a public transport accessibility index, were used in three separate regression models. This study also examined which accessibility measure has greater impact on the number of years that residents decide to stay in their current location. Socioeconomic characteristics and built environment factors were also employed in the analyses. Results of the models indicated that built environment variables have a statistically significant impact on number of years living in an address. However, accessibility indexes show greater impacts compared to other built environment factors. Of the socioeconomic variables, household size, car licence, income and work type have negative association with YL. Having a driving licence decreases the likelihood of living longer at the same address. The reason could be having more flexibility to move to farther areas. Household size also shows negative affect on YL. Increasing the size of the household may encourage households to move to larger dwellings. Living in flats/apartments as well as terraces/townhouses also negatively affect YL, whilst living in separate houses strongly encourages households to stay in their current location. Hence, the results reveal that those who own their homes are less likely to move and change residences. Housing type has been found to be one of the factors that affect residential mobility intentions in previous studies (Fattah, Salleh, Badarulzaman, & Ali, 2015). Overall, the empirical results suggest that the role of non-motorised accessibility is statistically significant in explaining residential location choice. Furthermore, the results of the current study indicate that accessibility has a greater impact on residential moves compared with other built environment factors. In other words, households are less likely to move away from a more accessible location. This result is consistent with research by Kim et al. (2005). In addition, the findings indicate that households who live in areas with higher accessibility to public transportation are more likely to stay longer at their current location. In summary, based on the literature, there is a significant gap in considering non-motorised accessibility on residential location. In comparison with the very limited previous work, the analysis presented

Table 3 Independent variables and their descriptions. Variables

Variable

Description

Age of respondent Gender Number of vehicles in household Individual income per week Having a driving licence Usual number of residents in household Type of dwelling Years lived at the same address Type of work Cycling Accessibility Index Walking Access Index Public Transport Accessibility Index Roadway Measure Connectivity Land use mix entropy index Population density

Note: 1. Licence (Yes and No) and sex (male and female) were defined as binary variables. 2. Dwelling type is converted into three dummy variables: separate house, terrace/townhouse, and flat or apartment; 3. Work type is converted into six dummy variables: full-time work, part-time work, casual work, unemployed, retired, other. 7

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Table 5 Outputs of OLR models for YL. Parameter

YL = 1 YL = 2 YL = 3 YL = 4 Age Sex (Male) LNC (Yes) PINC HHS Cars WT Full-time work Part-time work Casual work Unemployed Retired DWT Flat or apartment Separate house Terrace/townhouse CNTY PNDY RWM EI PTAI WAI CAI

M1

M2

M3

Coef.

Std. err.

OR

Coef.

Std. err.

OR

Coef.

Std. err.

OR

0.730 1.774 2.804 3.789 0.025⁎⁎⁎ 0.013 −0.273⁎⁎⁎ −0.040⁎⁎⁎ −0.159⁎⁎⁎ 0.280⁎⁎⁎

0.2389 0.2394 0.2407 0.2424 0.0017 0.0441 0.0706 0.0075 0.0215 0.0241

2.075 5.893 16.503 44.226 1.025 1.013 0.761 0.961 0.853 1.323

0.603 1.644 2.673 3.660 0.025⁎⁎⁎ 0.013 −0.266⁎⁎⁎ −0.041⁎⁎⁎ −0.146⁎⁎⁎ 0.284⁎⁎⁎

0.2379 0.2383 0.2395 0.2412 0.0017 0.0441 0.0706 0.0076 0.0214 0.0242

1.828 5.175 14.486 38.860 1.025 1.013 0.767 0.960 0.864 1.329

0.632 1.670 2.695 3.673 0.025⁎⁎⁎ 0.013 −0.280⁎⁎⁎ −0.039⁎⁎⁎ −0.142⁎⁎⁎ 0.253⁎⁎⁎

0.2387 0.2391 0.2404 0.2420 0.0017 0.0440 0.0705 0.0075 0.0214 0.0239

1.882 5.310 14.803 39.352 1.026 1.013 0.756 0.962 0.868 1.288

−0.552⁎⁎⁎ −0.530⁎⁎⁎ −0.419⁎⁎ −0.715⁎⁎⁎ −0.414⁎⁎

0.1617 0.1657 0.1776 0.1663 0.1591

0.576 0.589 0.658 0.489 0.661

−0.546⁎⁎⁎ −0.526⁎⁎⁎ −0.431⁎⁎ −0.719⁎⁎⁎ −0.413⁎⁎

0.1615 0.1656 0.1775 0.1661 0.1589

0.580 0.591 0.650 0.487 0.661

−0.549⁎⁎⁎ −0.512⁎⁎⁎ −0.374⁎⁎ −0.710⁎⁎⁎ −0.409⁎⁎

0.1611 0.1652 0.1770 0.1657 0.1585

0.577 0.599 0.688 0.492 0.664

−0.108 1.490⁎⁎⁎ −0.182 −0.010⁎⁎ 0.078⁎⁎⁎ −0.042⁎⁎ 0.040⁎⁎⁎ 0.207⁎⁎⁎

0.1681 0.1236 0.1458 0.0037 0.0212 0.0158 0.0135 0.0172

0.898 4.436 0.834 0.990 1.081 0.959 1.041 1.230

−0.095 1.462⁎⁎⁎ −0.180 −0.010⁎⁎ 0.058⁎⁎ −0.049⁎⁎ 0.018

0.1682 0.1237 0.1463 0.0037 0.0217 0.0158 0.0141

0.909 4.314 0.835 0.990 1.060 0.952 1.018

−0.074 1.439⁎⁎⁎ −0.132 −0.008⁎⁎ 0.137⁎⁎⁎ −0.048⁎⁎⁎ 0.045⁎⁎⁎

0.1673 0.1231 0.1456 0.0037 0.0204 0.0158 0.0138

0.929 4.218 0.876 0.992 1.147 0.953 1.046

0.178⁎⁎⁎

0.0152

1.194 0.128⁎⁎⁎

0.0165

1.137

Notes: (1) n = 8562; (2) YL as dependent variable is divided into five categories: < 7 years, 7 to 12 years, 13 to 20 years, 21 to 29 years and > 30 years. The last category is the reference level. (3) Significance codes: p < 0.001 ‘⁎⁎⁎’, 0.01 ‘⁎⁎’, and 0.1 ‘⁎’. (4) Overall goodness-of-fit: M1: Pseudo R-Square = 0.21; AIC = 21,461.167, Log Likelihood = −10,707.583; M2: Pseudo R-Square = 0.20; AIC = 21,469.787, Log Likelihood = −10,711.893; M3: Pseudo R-Square = 0.19; AIC = 21,550.681, Log Likelihood = −10,752.340.

connection between accessibility and choice of residential location.

here is distinctive, because it incorporates the impacts of walkability, bikeability and public transport accessibility on the number of years that residents stay in their current neighbourhood. While accessibility, and in particular, access to public transport can encourage residents to stay longer in their current location, policy makers may consider this to provide early delivery of healthy transport options in new suburbs. This will provide evidence and tools to assist public and private sectors in preparing transport options for residents in Melbourne's new suburbs as soon as they move in. In this way, politicians and other decision makers may achieve improved resident transport, health outcomes and transport infrastructure. Furthermore, the measurements described in this study are capable of being used by urban and transport planners as well as policy makers in relation to any proposed land-use development. Apart from the ease of understanding of both measurements, one of the greatest strengths of these measures is that they reflect land-use features in terms of diversity and intensity of activities. Furthermore, the research indicates the importance of non-motorised accessibility on residential willingness to stay at their current location. New approaches measuring walking, cycling and public transport accessibility were employed in regression models along with socioeconomic and built environment factors. The approaches used in this study are straightforward and simple to apply. Although the accessibility measurements were developed for metropolitan Melbourne, the approach is transferrable to other geographical areas/scales. However, due to the unavailability of data, this research did not include psychological factors in modelling. Therefore, future studies may consider those variables to obtain more accurate results. In addition, this study did not consider residential geographical movements, and, spatial analyses of residential moves may provide a better understanding of the

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