Empirical analysis of residential-job location independence is hinged on two major theoretical assumptions. The first posits a conditional choice process whereby individuals choose where to live first, and based on their home locations, choose where to work or vice versa. The second assumes a conjoint process whereby home and job location decisions are taken together at the same time without imposing a hierarchical structure.
The household interviews sought to gain insight into the residential-job location interdependence by verifying empirically, which of the two theoretical assumptions indicated above held true within the context of location choice decisions in the Kumasi Metropolis. This was done at two levels. Firstly, respondents were asked to indicate what their home and job location circumstances were when they first settled in the metropolis. At the second level, they were asked to reveal their last job and house moves since settling in the metropolis and to indicate the extent to which the two locations were responsive to each other during the move.
In response to the residential and job location choice relationship when they first moved into the metropolis, about 71% indicated that the decision regarding where their job locations would
be in the metropolis came sometime later based on their initial places of residence. On the contrary, for the remaining 29%, who were mainly households whose heads were moving into the metropolis for the first time in response to a job offer, their residential locations decisions were taken subsequently from their respective job locations. Thus, initial location choice followed a sequential process by which the place of residence was taken first followed by decision regarding the work-place location. Indeed, the fact that the nearly half of all households are engaged in home-based employment imply that finding a suitable residential location would almost always be the most important step.
Besides the sequence of choice during their initial job locations, the respondents were also asked to reveal their last house and job moves and whether one of the locations changed in response to the other. Results from the data shown that 52% of individuals interviewed had changed residential locations within the last seven years leading up to the time the interviews were conducted. In other words, these individuals and their households had moved from a previous home location within the metropolis to their current home locations. Asked if the change of residence was the result of job change, 10% said this was the primary reason. One percent indicated that job change was a secondary reason for change of residence. For most (89%) of these respondents however, job change was not the reason at all for moving to a new house. Instead, the main triggers of residential move included a lack of comfort and privacy, particularly for those who formerly lived with extended family relations in traditional compound houses. Other reasons included tenancy contracts ending without renewal, troubles with previous landlords, the prospect of moving into the metropolis to search for new job opportunities, the need to live closer to their jobs and the need to join relatives already living in the metropolis.
With regards to recent job location changes, the results showed that whereas 15% had changed job locations in the last seven years, the remaining 85% had not done so. In 84% of the cases involving job location change, the change did not lead to residential moves. Among the few individuals who had changed job locations, only 29% indicated that the change was necessitated primarily by changes in residential locations. Whereas eight percent reported that change of residence was a secondary reason that accounted for job location change, the majority (63%) indicated that residential location change was not a reason at all for their last job location change. Instead, poor remuneration at previous jobs, job collapse leading to
retrenchments and the decision to start their own private businesses were given as the major reasons for job change and hence change in job locations among most the respondents.
In summary, three key insights can be drawn from the above findings regarding the residential- job location choice interdependence. Firstly, for most households and individuals, the initial location choice process followed the sequential choice process whereby residential location decisions were taken first. With the established home locations of households as reference point, individuals make job location decisions considering proximity to the home and essential amenities, access to markets especially in the case of petty traders and sales workers in the informal economy as well as the opportunities for well paid jobs. Secondly, for nearly half (i.e. 48%), of individuals, their residential locations have remained stable since moving into the metropolis. Job locations, however, were generally more stable than residential locations given that 85% of the respondents had not changed jobs in the last seven years. The fact that job openings unlike residential vacancies are very limited and hard to find could explain the stability of job locations. Thirdly, for most of the respondents, the decision to move to a new house was not job-related, neither was the decision to change job locations triggered by residential moves in most cases.
4.10 Chapter summary
This chapter has presented an empirical analysis of the determinants of residential location choice of households, the determinants of job location choice of individual members of the household and the interdependence between the two choice sets in the Kumasi metropolis using data obtained through a cross-sectional survey.
Using principal component analysis, four main factors were identified as important macro and meso level considerations across all households in their residential location choice. These factors were; ‘proximity to major infrastructure and amenities’; ‘family ties and social
networks’; ‘character of neighbourhood’; and ‘proximity to core activity locations’ (i.e. work-
place and school) of members of the household. Results from a series of hierarchical multinomial logistic regression models showed that factors including household income, educational attainment of heads, family size, and urban-zone of residence determined differences in dwelling type preferences and housing tenancy choice.
Furthermore, the analysis provided vital empirical insight into the job location patterns in the Kumasi metropolis. Overall, the results showed that lower income levels, residence in the historical-core of the metropolis, low-skill levels and involvement in small scale commercial activities in the informal economy were the key determinants of whether an individual had a home-based or non-home-based employment. A principal component analysis of job location factors further showed that two major factors, namely; ‘proximity to the place of residence and
essential amenities’, as well as ‘job availability and higher wages’ determined individuals’ job
location choice.
The final section of this chapter addressed an important question regarding the residential-job location choice interdependence in the Kumasi metropolis. Results of the analysis revealed that most of the individuals interviewed considered where they would live first, and based on the residential location outcomes, decided where to work. In terms of residential and job relocation decisions, the analysis showed that generally, residential and job locations remained stable and unchanged over several years. Notwithstanding, residential relocations occurred a little more frequently than job location changes. For most of the respondents, the decision to move to a new house was not job-related, neither was the decision to change job locations triggered primarily by residential moves.
The analysis presented in this chapter will provide the backdrop for the analysis of the patterns of daily spatial interaction between the home and work-place in the case study metropolis. The next chapter deals with this subject, focusing on work trip frequency, work trip production and attraction patterns, travel mode choice and commuting times and travel costs.
CHAPTER FIVE: HOME-WORK MOBILITY PATTERNS,
TRANSPORT MODE CHOICE AND TRAVEL COSTS—AN
EMPIRICAL ANALYSIS
5.1 Introduction
Patterns of urban mobility reflect individuals’ need to participate in various activities located within the urban area. As a concept, urban mobility encompasses many dimensions including trip purpose and frequency, trip origins and destinations, travel mode choice, route choice, travel distance, travel time and transport costs.
The purpose, origin and destination of daily trips are influenced by the long-term urban location decisions that have shaped the spatial distribution of urban functional activities such as the work-place, the place of residence, and ancillary facilities including shopping and recreation. The resulting, trip production and attraction patterns could therefore be quantified either as flows between specific points of activity locations or as flows between aggregate urban-zones containing many activities such as Traffic Analysis Zones (TAZs).
The daily human interactions with urban activity locations at the different spatial scales also involves several short-term decisions including travel mode choice, route choice and the time of day in which trips are planned to begin and end. The prevailing urban structural conditions, interact with the socio-economic characteristics, attitudes and lifestyle preferences of the individuals involved to shape these choices.
The objective of this chapter is to examine empirically, mobility patterns in the Kumasi metropolis, the case study area for this research. It examines and quantifies mobility patterns associated with the residential and job location combinations of individual workers in the metropolis. The empirical analysis of mobility patterns presented here, therefore, builds on initial analysis of residential and job location patterns in the metropolis presented in Chapter four. The analysis examines the various aspects of flows and interactions between the home and work location pairs of individuals including home-work trip frequency, trip origins and destinations, travel mode choice, travel distance, travel time and commuting costs.
5.2 Overview of the data and statistical analysis methods
The analysis of home-work mobility characteristics presented in this chapter is based on travel data obtained from a sample population of 1,558 workers through a cross-sectional survey14 in the Kumasi Metropolis. These individuals were adult working members from the 665 randomly selected households that constituted the sample population for this research. Thus, in addition to obtaining travel data at the level of the individual, the analysis is positioned within the wider household to which the individuals are members to explore the extent to which household attributes such as income, family size, car ownership and residential locations affect individual travel choices. In addition to the primary data on travel, secondary data on Traffic Analysis Zone (TAZ) designations was obtained from the Urban Roads Department of the metropolis. The TAZ dataset, comprising aggregate zones delineated based on unique functional characteristics, constitutes the spatial scale to which the analysis of work trip production and attraction patterns obtained from the travel data is anchored.
A detailed discussion of the statistical analysis methods used in this chapter, including mathematical representations and assumptions has been presented in Chapter three, the methodology chapter covering the two empirical studies presented in this thesis. In view of this, the themes of the analysis and the methods used is presented briefly in this section.
Both descriptive statistics and regression analysis methods are employed in the data analysis. Home-work trip production and attraction among the TAZs are extracted from the travel data and analysed using basic descriptive statistics. To examine the determinants of travel mode choice, a series of Binary Logistic Regression (BLR) models are fitted to the data. The first BLR analysis examines the determinants of private car ownership among households of the individuals interviewed. The method is also applied to examine the determinants of active and motorized transport mode choice, as well as choice between different public transport modes available in the metropolis. Finally, using descriptive statistics, correlation analysis and linear regression methods, data on travel times and costs associated with home-work commuting and their determinants are analysed.
14 Detailed discussion of the sampling approach, study variables and the survey instrument that was used to elicit
data on individuals’ mobility characteristics is presented in Chapter three, which is the main methodology chapter covering the empirical analysis in this chapter.
5.3 Chapter organization
The remainder of this chapter is organized into five main sections. To provide background understanding of the urban structural conditions within which daily mobility patterns in the metropolis occur, the analysis opens with a discussion of the metropolitan functional structure and the existing TAZ designations in the metropolis. Next, analysis of home and work trip production and attraction patterns at the level of the TAZs is presented. In the third section, individual workers’ transport mode choice considerations are analysed followed with the analysis of the determinants of transport mode choice in the fourth section. The transport mode choice analysis focuses on the determinants private car ownership and use, active and motorised transport choice, and choice between public transport options by commuters in the metropolis. The penultimate section presents an analysis of travel times and costs associated with home-work commuting in the metropolis. The final section of this chapter provides a summary of the results presented highlighting the key findings.