2024 年 12 巻 4 号 p. 159-180
The development of urban mobility, perceptions, influences, and scenarios is largely determined by socio-economic changes and cultural diffusion, which drive the spatial interaction of settlement areas. Therefore, this research aimed to analyze the factors that determine, influence, and map models of lifestyle changes and individual mobility perceptions in socio-economic spatial interactions. Data collection was conducted through observation and a questionnaire from 398 samples in the settlement of Bumi Tamal Anrea Permai and the surroundings. The multivariant statistical analysis methods was used to test the factors and perception mapping with multidimensional scaling was processed with SPSS 25 software. The results showed that individual mobility is characterized by changes in lifestyle and socio-economic, such as increase in private vehicle ownership in suburban. Based on the scenarios created, perceptual model of individual mobility and spatial interaction was formed to determine the increase in private vehicle ownership. The results improved the substantive concept of spatial flows through the integration in the city transport system. This research would help formulate the ideal transport sector policy for urban and suburban development to realize the sustainability of cities in the future.
The development of major cities in ASEAN Member States (AMS) is persistent due to the benefits and opportunities effected by urbanization, leading to economic growth, urban and peri-urban sprawl, including the establishment of new urban corridors. This socio-economic growth also influences lifestyle changes, such as ownership of private residences and vehicles (ASEAN, 2022; Ismiyati, I, Soetomo et al., 2011). Urbanization and city development had led to dense populations in cities and metropolitans, increasing challenges associated with mobility, infrastructure (Farzaneh, de Oliveira et al., 2019; Riza, Bruehl et al., 2024), social habits and behaviors, transport innovations, as well as improving the quality of built environment (Vinci and Di Dio, 2014).
Shared mobility services are driving demand requirements such as parking, no-vehicle ownership, including safe and convenient emission-efficient methods increasingly focused on by manufacturers (Baptista, Melo et al., 2014; Bimbraw, 2015). However, current mobility trends tend to receive less attention from public transport in the third space dimension (Al Haddad, Chaniotakis et al., 2020). A new paradigm in the information age had pushed the elemental flow into a space where technological and physical activities were combined (Xi, Zhen et al., 2016). This phenomenon varies due to the transport problems that plague metropolitan cities in Southeast Asia, including the trend towards a car-dependent community (Barter, 2004). Based on the diverse transport modes, services offered, differences and uniqueness of socio-economic features, system characteristics, and movement patterns of the population in Indonesia, the implemented policy cannot be used as an intervention tool in the oversight and control of transport problems (Manaf, 2021). According to the Indonesian Statistical Report 2023, the number of motorized vehicles in the country was anticipated to reach 125.3 million units by the end of 2022. The number of motorbikes and four-wheeled vehicles were also estimated to reached 48.9 million units or 64% and 17.2 million units, respectively from 2012 to 2022. Additionally, the number of four-wheeled vehicles was predicted to increase by 6.74 million units, or 65% (Badan Pusat Statistik, 2023). Lifestyles associated with suburban living choices and vehicle ownership preferences are inadequate for efficient mobility, in terms of cost, and density consequences (Ismiyati, I. and Hermawan, 2018). It can be viewed from another perspective, where population mobility is influenced by work, with private vehicles mainly used for various purposes including education, offices, and trade activities (Somantri, 2013). This research differed because the rapid development of settlement infrastructure network in peri-urban areas was typically affected by lack of integration due to private developers, who prioritized housing over infrastructure improvement (Nong, Fox et al., 2021).
In accordance with the current development of suburbs, spatial integration and urban agglomeration processes causes changes in structural patterns, including the initial typology and morphology, leading to densification as well as social and economic complexities (Amri, Erwin, Selintung et al., 2023b; Surya, Ahmad et al., 2020). The processes were observed in terms of changes in land use for socio-economic development activities (Surya, Ahmad et al., 2020). However, it was conceptualized that spatial integration is the merging of city and regional systems into a unified whole, or the process of adjusting different elements into a series of activities with harmonious functions (Liu, He et al., 2016). Spatial interactions tend to influence patterns of urban behavior, including economic and social processes. Meanwhile, interaction is regarded as a midpoint between centralized and decentralized methods that focus on the growth of metropolitan areas and dispersal of resource investments, respectively (Li, Wang et al., 2016). The development of Makassar suburban settlement areas depicts the dynamics of individual mobility and socio-economic interactions, as well as relationship with increasing private vehicle ownership, household economic factors, online-based technologies, and improved accessibility, important for implementing efficient policy directions and transport infrastructure that tends to accommodate these promising trends.
Transportation strongly affects the economic activities in any area by improving accessibility and mobility, which has far-reaching effects on geographic activities, urban land use configuration, and regional competitiveness (Banister and Berechman, 2001; Collier and Venables, 2016). The currently fabricated mobility paradigm increasingly faced economic, ecological, and social constraints in urban areas (Fournier, Boos et al., 2020). Transportation systems, closely associated with socio-economic changes have both positive and negative impacts, such as congestion, accidents, and mobility gaps (Rodrigue, J.-P., 2020). Furthermore, mobility is related to specific urban activities and land uses, with the potential to generate and attract a variety of movements. This complex relationship is associated with the following factors recurrence, income, urban form, density, development, and technology levels. Urban mobility consist of three broad categories, namely collective, individual, and freight transportation.
Individual mobility, characterized by the dominance of private vehicle modes, is closely related to changes in household income and associated lifestyle alterations (Morrow-Jones, 1989). The materialization of shared mobility options, such as ride and car-sharing, introduced a psychological shift towards more collaborative transportation solutions (Koning, Roald et al., 2020). These trends, combined with the rise of on-demand delivery services, have the potential to influence individual travel behavior (Koning, Roald et al., 2020; Shaheen and Cohen, 2021). However, recent research focused on the growing trend towards a more sustainable lifestyle among various demographic groups, including the desire to share rather than possess personal assets. Mobility-on-demand services, such as transportation network companies, have gained market share in most cities globally, and the relevance is intended to increase with the introduction of autonomous vehicles (Engelhardt, Dandl et al., 2022). Additionally, last-mile delivery services function as a substitute for personal trips, with consumers taking advantage of just-in-time delivery, potentially contributing to significant changes in travel behavior (Shaheen and Cohen, 2021). The interplay between individual mobility, household income, and lifestyle changes is complex and multifaceted. The transition to shared mobility solutions, facilitated by the sharing economy models, has prompted new transport options that cater to evolving consumer preferences and transportation needs.
In the Global South, limited mobility due to rising per capita income and urbanization has led to traffic congestion and increased use of private vehicles (Mittal and Biswas, 2019). Therefore, it is essential to conduct individual mobility research on the scale of suburban areas, since community perception is mainly affected by personal transportation modes, namely cars, motorcycles, walking, and cycling. The tendency of spatial and socio-economic interaction patterns for basic needs such as markets, sports facilities, restaurants, shops, education, clinics, and offices increasingly dominate the use of private vehicles.
Private car ownership, are currently prioritized in Makassar City, leading to changing behavioral patterns, lifestyle development, and technological advancement (SchipperL, 1989; Weber and Perrels, 2000). The people tend to experience social changes associated with the transition from traditional to urban community such as competitive lifestyle characteristics, including individualism, free living, and materialism (Akil, Yudono et al., 2014). However, no research had directly explored the relationship between increased vehicles and changes in lifestyle and mobility. Lifestyle changes refer to a form of social status alteration in suburban community. This led to the proposed hypothesis H1 Increase in private vehicles per dwelling unit affects household lifestyle changes.
Urban travel behavior and household income during economic crisis promote sustainable mobility (Papagiannakis, Baraklianos et al., 2018). Additionally, family income and size impact the share of housing and transportation expenditure (Makarewicz, Dantzler et al., 2020). Household income has a positive impact on mobility (Dargay, 2007) and is the subject of related investigation in transport systems. However, no research had directly analyzed the causal relationship between increased income and lifestyle changes. This concept focused on changes in social status, a result of purchasing private vehicles prompted by the sales policy, thereby leading to the formulation of hypothesis H2 which stated household income affected changes in lifestyle.
Future urban mobility and transportation were developed based on uncertainties arising from the driving forces of technological and social factors, environmental issues, economic growth, and governance (Vallet, Puchinger et al., 2020). Meanwhile, technological and social factors, including economic, environmental, and political influences were combined to formulate mobility and policy implications for the elderly (Lyons, Rohr et al., 2021; Shergold, Lyons et al., 2015). The new mobility paradigm also relied on the advent of new practices and technologies in reconstructing socio-economic activities and cultural diffusion (Merriman, 2020). Furthermore, a strong relationship exists between mobility and socio-economics with variations over time (Long and Ren, 2022). The combination of technology and social change creates a scenario for transitioning transportation to the digital age (Hannon, Knupfer et al., 2020; Lyons, 2015; Vallet, Puchinger et al., 2020). The conceptual demand for online-based technology is part of the transport system. However, no research had directly evaluated the cause-and-effect relationship between the need for online application-based technology and lifestyle changes at the scale of suburban units. This led to the formulation of hypothesis H3 that online-based technology affected changes in household lifestyles.
The increase observed in the accessibility of the area and number of private vehicle ownership per housing unit was due to social changes perceived by alterations in household income. This also tends to affect the increase in land use activities, prompting socio-economic interactions in Bumi Tamalanrea Permai (BTP) settlement area, Makassar City (Amri, Erwin and Manaf, 2023; Amri, Erwin, Selintung et al., 2023b). Improving transportation accessibility in the context of linkages and interdependencies is closely related to lifestyles (Kuklina and Filippova, 2019). The conceptual accessibility of the area was widely observed in the aspect of transportation. However, no research had directly explored the cause-and-effect relationship associated with individual mobility from the viewpoint of suburban units. This led to the formulation of hypothesis H4 that accessibility affected changes in household lifestyles.
Improved mobility is associated with reduced accessibility in the long term due to land use change (Kuklina and Filippova, 2019). Individuals with limited mobility were rated twice in respect to many accessibility elements than those without limited mobility (Peña Cepeda, Galilea et al., 2018).s Therefore, it is important to relate the influence of increased accessibility on individual mobility in perceptual model to suburban. In reality, private vehicle ownership, online facilities, and motorcycle taxi modes dominate the increase in accessibility, resulting in congestion on major roads. This led to the formulation hypothesis H5 that improved accessibility affected individual mobility.
The ever-increasing number of cars, specifically private owned had changed social life, as well as revolutionized travel experience and mobility. This led to the reshaping of urban transport systems, where human mobility and movement behavior with private car use is poorly understood (Xiao, Xiao et al., 2022). However, no research had directly analyzed the cause-and-effect relationship associated with individual mobility from the perspective of suburban units. This led to the formulation of hypothesis H6 that increase in private vehicles per dwelling unit affected individual mobility.
The increasing popularity of new mobility concepts, such as car sharing, reflects the changing attitude of community. Furthermore, several decisions concerning meeting this demand are being made based on rational rather than emotional considerations (Kuemmerling, Heilmann et al., 2013). These focused on aspects of uncertainty associated with future lifestyles to develop a three-pronged accessibility system, namely physical mobility, proximity, and digital connectivity, with digitalization influencing daily lives (Lyons, Mokhtarian et al., 2018). Socio-economic growth leads to lifestyle changes, specifically in the ownership of private residences and vehicles (Ismiyati, I, Soetomo et al., 2011). Therefore, changing lifestyles and mobility of individuals needs be considered in relation to suburban dwellings, leading to the formulation of hypothesis H7 that changes in lifestyle affected individual mobility.
Social interaction is highly relevant in analyzing the hierarchical pattern of mobility (Alessandretti, Aslak et al., 2020), or movement across the city (Cho, Myers et al., 2011). Moreover, people belonging to the same socio-economic status prefer to interact with one another based on variation in spatial interactions, sustained when considered between cities (Lenormand and Samaniego, 2023). The cause-and-effect relationships associated with socio-economic spatial interactions was evaluated from the suburban viewpoint in respect to individual mobility. This led to the formulation of hypothesis H8 that individual mobility affected socio-economic spatial interaction.
The structural model of the following variables, increasing private vehicles per dwelling unit, income, online-based technology needs, and improved accessibility did not explain the relationship concept between socio-economic conditions and cultural diffusion in-depth. Meanwhile, lifestyle changes, increase in private vehicles per dwelling unit, and improved accessibility are determinants of individual mobility and socio-economic space interaction. The objective of this research focused on investigating the factors that determine, influence, and map the perception of lifestyle change, individual mobility, and social-spatial interactions in BTP settlement and surrounding areas in suburban of Makassar City based on the novelty of the structural path and perception mapping models. This method was used to analyze the theoretical reality based on the perception of the people to determine the main scenario and purpose of the problem. Additionally, this led to the formulation of the following research questions:
1. What are the factors that determine lifestyle changes and individual mobility in suburban settlement areas?
2. How does the increase in private vehicles, changes in household income, the need for online technology, and improved accessibility of the area affect lifestyle changes, individual mobility, and socio-economic spatial interactions?
3. How does the perception mapping model affect lifestyle changes and individual mobility in the socio-economic space interaction of suburban areas?
This research compelled policymakers to focus on the transport sector in urban development. Furthermore, urban mobility adopted substantive variations where space and elemental currents prompted the integration of technological aspects towards socio-economic and lifestyle changes in mobility and space interactions. The conceptual framework is shown in Figure 1.
This research adopted a quantitative method due to the following considerations a) testing the theoretical reality of previous investigations, b) developing and interpreting the results of variable relationship, c) finding scenarios and concepts based on points (a) and (b) to produce a perception model.
Research AreaThe research area is BTP settlement and the surrounding comprising suburban areas situated in Makassar City as shown in Figure 2. Additionally, BTP settlement and the surrounding has an area of ± 255.6 hectares and 170.69 hectares, respectively. The neighboring area consist of rental flats in Kodam VII Wirabuana, Telkomas Housing, Kampong-Bontoramba settlement, Bangkala, Cokro, Buntunsu, and Katimbang (Amri, E, Selintung et al., 2021; Amri, Erwin, Selintung et al., 2023a). The use of suburban settlements as a case study was due to the following reasons (a) BTP settlement and the surrounding are experiencing rapid growth, characterized by complex socio-cultural and economic changes, as well as heterogeneous community composition, (b) private vehicle ownership per housing unit continues to rise, (c) there is an increase in socio-economic activity, driven by the growth of informal activities, education, health, government and private agencies, services, and trade, all of which incite individualistic mobility, and (d) this case study aimed to map community perceptions of socio-economic integration, technology, accessibility, and cultural diffusion of interactional mobility.
Source: Authors’ elaboration; folder© 2021 google
Method of Collecting Data
The research population comprised people who had resided in BTP and the surrounding for 5 years. This area administratively located in Biringkanaya and Tamalanrea Sub-districts, has a total population of 103,322 people. Meanwhile, the research sample consisted of 398 respondents, selected using the Slovin formula, because the population size is known (Yamane, 1973). This formula was applied based on the large heterogeneous population in suburban settlement areas of Makassar City. Furthermore, the sample was determined using a multistage random, or two-stage sampling method. The provisions include heterogeneous community characteristics, namely random cluster and individual sampling. The formula used in sample withdrawal is stated as follows:
Where:
n = sample size
N = population size
e = margin of error (in decimal form, for example 5% = 0,05)
Data Collection instrumentsThe primary information collected were quantitative data gathered through surveys, covering (i) the incomes of community in suburban area, (ii) the ownership conditions of personal vehicles per household and suburban unit, (iii) the frequent use of both personal and online-based transport modes, and (iv) the development of activities and socio-economic interactions in BTP settlement area and surrounding. The preliminary assumptions comprised the formulated hypotheses, namely (a) a relationship between income, increased personal vehicle, and online-based technology needs in respect to lifestyle changes, (b) increased private vehicle, and online technology needs, including lifestyle changes towards individual mobility, and (c) the mobility of individuals against socio-economic spatial interactions in suburban Makassar settlements.
The first phase, sampling by random cluster include (i) communities residing on the major road that is the center of socio-economic activities such as education, health, offices, services, and trade, (ii) environmentally blocked and connectivity roads of the surrounding residents where most communities are engaged in daily interaction and activities, as well as (iii) communities residing in blocks A to J (single block) and blocks AA to AF (double block) who engage in individual mobility and interaction.
The questionnaire instrument was distributed to obtain data on the perceptions of suburban area regarding socio-economic alterations and cultural diffusion through changes in household lifestyles associated with individual mobility relationships and socio-economic space interactions. Furthermore, changes in socio-economic conditions were observed in private vehicle ownership, household income, online-based technology needs, and accessibility. Individual mobility was viewed based on the dominant selection of private vehicles and online-based transportation to support specific activities. This was realized by using private vehicles and technology in the area. Socio-economic spatial interaction was viewed based on social relations associated with the economic activities in BTP suburban area and surrounding, whose indicators were measured the questionnaire using the scale (1) strongly disagree, (2) disagree, (3) less agree and disagree, (4) agree, (5) strongly agree. The use of a 5-point Likert scale simplified measuring attitudes and opinions due to the effortlessness, ease of analysis, and consistency with social research standards. Open-ended questionnaires were collected through the Google Form media from October to December 2023. The 398 respondents were selected using an individual purposive sampling method, mainly aimed at those residing in BTP settlement and surrounding. The provisions for the withdrawal of the sample were based on the conditions that these people had resided in BTP settlement and surrounding for 5 years.
Data Analysis MethodThe acquired data were explored by adopting path and perception mapping analyses, aspects of statistical and multi-dimensional scaling methods, both of which used SPSS software version 25. Descriptive statistical analysis in the form of frequencies, percentages, mean and standard deviations was used to evaluate the data obtained quantitatively. Meanwhile, statistical tests conducted included a one-way analysis of variance (ANOVA), an independent sample t-test, Pearson correlation, and a chi-square test (Jafari, Zadehahmad et al., 2023). The structural path model was generated based on the investigated variables. This research was divided into three stages, namely (a) in the first step, cultural and socio-economic diffusion were initially developed through the relationship between lifestyle changes (dependent variable) measured by observing the increase in the number of private vehicles per dwelling unit, household income, need for online-based technology and the rise in area accessibility (independent variables). The properties that have the highest correlation with lifestyle changes were considered as structural models in the first class of variables. (b) In the second step, the results of the relationship between lifestyle changes, increase in the number of private vehicles per dwelling unit, transportation mobility (dependent variable) and rise in the area accessibility (independent variables) were evaluated to generate the second structural model. (c) In the final step, the results of transport mobility and socio-economic spatial interactions led to a third-path correlation relationship, regarded as the main structural model. After determining the correlation coefficient, including the direct and indirect effects, the path analysis diagram was schematized using the Edrawmax software. This research proposed a relationship model using the variables in the following structural equation:
χ5 = β1χ1 + β 2χ2 + β 3χ3 + β 4χ4 + e (2)
γ = β 5 χ5 + β 6χ6 + β 7χ7 + e (3)
ζ = β8γ+ e (4)
The evaluation process was carried out using the path analysis equation shown in Figure 4. Furthermore, multidimensional scaling (MDS), a mathematical method and an aspect of multivariate statistics was applied to the distance matrix for the purpose of data reduction (Rencher and Christensen, 2012). MDS is used to measure Euclidean distances between pairs of objects. This distance is defined as the standard measure of proximity used to visualize data structures (Adnan, Ahmad et al., 2013; Groenen and Borg, 2015). Euclidean distance is a basic MDS measure developed with new methods that add or provide an alternative to this measurement. The development process reflects ongoing efforts to improve the accuracy and application of MDS in various domains (Adnan, Ahmad et al., 2013; Bai, Bai et al., 2017; Shang, Shang et al., 2019; Xuan, Ma et al., 2015). Additionally, the Euclidean metric is a function of d RM x RM → R, which expresses any two vectors, namely objects, individuals, and projects i = i1,..., im and j = j1,..., jm and m = 1,...,. M depicts dimensional space with an indication of the distance between any two vectors (Esmalifalak, Ajirlou et al., 2015). Mathematically, the Euclidean distance between vectors i and j was defined as follows:
Where:
M = 1, 2, ..., m
im = value of variable m for vector i
jm = value of variable m for vector j.
Stress is the normalized least squares index of the fit between the estimated and transformed distances, s(ζij) (Adnan, Ahmad et al., 2013). The models were used to measure stress levels, as stated in Equation 2:
Where:
ζ(ij) = dissimilarity between the i-th and j-th objects
dij = original distance between the i-th and j-th objects
Perception maps prioritized ideal points (IP) when showing objects or variables against one another. IP was also used to determine the selection of an exceptional scenario, generated based on the significance of a point representing the expected feature improvement. Additionally, timely representation is a method of understanding the use of Euclidean distance (Hair, Black et al., 2010).
Based on the statistical test stage, the coefficient of determination (R2) showed that the existing independent variables were adequately used to explain lifestyle changes because it had an R21 value of.170. This implied that 17% of the latent variable namely lifestyle changes was properly explained by the independent variables, including increase in private vehicles per dwelling unit, changes in household income, online-based technology needs, and rise in accessibility. Additionally, increase in private vehicles per dwelling unit and regional accessibility were used to properly explain individual mobility because it had an R21 value of 0.323.4. The influence of the independent variables on the dependent one such as individual mobility had R22 of 32.3%. This showed that the model was characterized by strong, medium, and weak predictive power for in-sample data, and was used to determine the appropriate model as shown in Table 1.
| R | R Square | Adjusted R Square | Std. Error of the Estimate | Change Statistics | |||||
|
|
|
df1 |
|
|
|||||
| .412a | .170 | .161 | .59102 | .170 | 20.096 | 4 | 393 | .000 | |
| Model 1: a. Predictors: (Constant), γ, χ1, χ4 | |||||||||
| .568a | .323 | .317 | .47095 | .323 | 62.534 | 3 | 394 | .000 | |
| Model 2: b. Predictors: (Constant), χ4, χ5 | |||||||||
| .229a | .153 | .150 | .55551 | .153 | 22.008 | 1 | 396 | .000 | |
| Model 3: c. Predictors: (Constant), ζ | |||||||||
Notes: **t-value is below 1.96 and *p<0.05
Path coefficient testing was carried out to determine the feasibility of the hypothesis in the structural relationship model. Meanwhile, a relationship is feasible when the calculated t-value is greater than the t-table. The effect is reflected by a positive value of the path coefficient with a significance t-value > 1.96 for α = 5% or a p-value < 0.05. The increase in private vehicles per dwelling unit affected lifestyle changes with a coefficient and t-value of 0.047 and 1.994, respectively. Changes in household income had a positive and significant impact on individual mobility, with a coefficient and t-value of 0.000 and 4.864, respectively. Online-based technology affected lifestyle changes, with a coefficient and t-value of 0.000 and 3.713, respectively. Improved area accessibility significantly impacted lifestyle changes, with a coefficient and t-value of 0.000 and 4.177. The second pathfinding showed that an increase in private vehicles per dwelling unit had a negative and significant effect on individual mobility, with a coefficient and t-value of 0.016 and -2.427. Additionally, lifestyle changes significantly impacted individual mobility, with a coefficient and t-value of 0.000 and 3.544. Increased accessibility also affected individual mobility, with a coefficient and t-value of 0.000 and 11.942. The third pathfinding, in respect to the path coefficient and t-calculated values, implied that socio-economic spatial interactions significantly impacted individual mobility with a coefficient and t-value of 0.000 and 4.691, respectively as shown in Table 2. Based on the results of testing the path coefficient, it was proven that all eight hypotheses proposed was accepted.
|
|
Unstandardized Coefficients | Standardized Coefficients | t |
|
|
|
|
Std.Error | Beta | |||
| (Constant) | 1.203 | .327 | 3.684 | .000 | |
| Increase in private vehicles per dwelling unit | .066 | .033 | .093 | 1.994 | .047 |
| changes in household income | .228 | .047 | .227 | 4.864 | .000 |
| Online based technology needs | .210 | .057 | .175 | 3.713 | .000 |
| Improved area accessibility | .184 | .044 | .198 | 4.177 | .000 |
| Model 1: a. Dependent Variable: Lifestyle changes | |||||
| F count = 20.096 > F table = 7.020 | |||||
| (Constant) | 1.945 | .191 | 10.180 | .000 | |
| Increase in private vehicles per dwelling unit | -.064 | .026 | -.102 | -2.427 | .016 |
| Lifestyle changes | .126 | .036 | .154 | 3.544 | .000 |
| improved accessibility of the area | .456 | .038 | .516 | 11.942 | .000 |
| Model 2: b. Dependent Variable: individual mobility | |||||
| F count = 62.534 > F table = 13.870 | |||||
| (Constant) | 3.372 | .147 | 22.915 | .000 | |
| socio-economic spatial interactions | .175 | .037 | .229 | 4.691 | .000 |
| Model 3: c. Individual mobility | |||||
| F count = 22.008 > F table = 6.792 | |||||
Notes: **t-value is below 1.96 and *p<0.05
In 1989, the government developed large-scale suburban areas through Perumnas and this marked a period of significant growth in the northern suburbs of Makassar City. Perumnas is a pioneer in the provision of housing, suburban areas, and flats for the lower middle-income class (Amri, Erwin, Selintung et al., 2023b). The designation of land and building ownership is currently dominated by the middle- and upper-classes. Furthermore, changes in socio-economic stratification in suburban areas depict the tendency of community to experience variations in income levels. Respondents stated that people earning 3 to 5 million per month dominated the reality conditions in the field, followed by those earning above 5 million.
Figure 7. a) Household income level; b) Household private vehicle ownership condition per dwelling unit
The present research stated that changes in household income affected lifestyle, however this is inconsistent with the findings of Dargay (2007) and Rodrigue, J., Comtois et al. (2016). The results obtained also focused on the fact that changes in household income does not have a direct relationship with mobility. Although, it had a significant relationship with lifestyle changes, leading to individual mobility. The variation in social stratification based on changes in household income due to the desire for certain lifestyle needs depends on two conditions, namely a) consumer households that spend income on related needs and lifestyle and b) producer households that use the house to engage in production processes.
Figure 8. a) Use of online application-based technology; b) Use of online-based private and personal modes of transport
The increase in private vehicles per dwelling unit tend to affect lifestyle changes, and individual mobility. This is consistent with the theoretical research by Weber and Perrels (2000). Furthermore, the results obtained showed that changes in community, especially suburban households had experienced a shift from pete-pete public transport to private vehicles and online-based mode. Weber and Perrels (2000) stated that changes in community experienced an increase in private car ownership and modal segregation, while excluding other modes. Suburban settlements in Makassar City experienced increased vehicle ownership, including both cars and motorbikes, with a rise of > 1 unit per house.
Changes in lifestyles affected individual mobility, and this was consistent with the research by Papagiannakis, Baraklianos et al. (2018), that household behavior and income prompted sustainable mobility. However, the results of this investigation showed a more complete result where lifestyle changes including the desire to keep excess personal vehicles, variations in household income, the need for online-based technology and increased accessibility led to alterations in individual mobility. Household incomes and online-based technology do not directly affect individual mobility through lifestyle changes. In addition, lifestyle changes also significantly affected individual mobility, and this was in line with the investigation by Shergold, Lyons et al. (2015). The results obtained proved that socio-economic variations characterized by household incomes and the need for online-based technology incited lifestyle changes and individual mobility.
In accordance with this perspective, individual mobility affected socio-economic and spatial interactions. This result is in line with the research by Alessandretti, Aslak et al. (2020), and Cho, Myers et al. (2011). Individual mobility refers to a lifestyle change determined by the dominance of private modes of transport, such as cars, motorbikes, and the reduced desire for pete-pete public transport. Moreover, the lack of proper mass transport motivated the magnitude of individual mobility in suburban areas, both internally and externally in suburban of Makassar City.
Scenario Analysis of Mobility Formation in InteractionAt this stage, the starting point focuses on generating a proximity or square matrix by considering the Euclidean distance between the variables. Table 3 shows the matrix with IP types plotted against optimally scaled data (differences) for the subject.
| χ1 | χ2 | χ3 | χ4 | χ5 | γ | Ζ | |
|---|---|---|---|---|---|---|---|
| χ1 | 0.000 | 2.781 | 1.538 | 1.538 | 2.781 | 2.781 | 1.538 |
| χ2 | 2.781 | 0.000 | 1.538 | 1.538 | 1.538 | 1.538 | 2.781 |
| χ3 | 1.538 | 1.538 | 0.000 | 2.781 | 2.781 | 1.538 | 2.781 |
| χ4 | 1.538 | 1.538 | 2.781 | 0.000 | 1.538 | 1.538 | 1.538 |
| χ5 | 2.781 | 1.538 | 2.781 | 1.538 | 0.000 | 1.538 | 2.781 |
| γ | 2.781 | 1.538 | 1.538 | 1.538 | 1.538 | 0.000 | 2.781 |
| ζ | 1.538 | 2.781 | 2.781 | 1.538 | 2.781 | 2.781 | 0.000 |
Based on the data processing results of community perceptions of BTP settlement and the surrounding obtained using the multidimensional scaling method, a position map of increased private vehicles per dwelling units, changes in household income and lifestyle, online-based technology needs, improved accessibility, individual mobility, as well as socio-economic space interactions was generated with a stress value and an index of fit (RSQ) of 0.18087, or 18.087%, and 74.062%, respectively. This implied that the stress value was in the fair category. Stimulus coordinates showed that the perception of each variable was expressed in two dimensions. In Table 4, the hierarchical incorporation of stimulus coordinates in the ideal position map was based on the increase in private vehicles, changes in household income and lifestyle, the need for online technology, increased accessibility, individual mobility, and socio-economic space interactions. Additionally, perceptual map had three dimensions, as shown in Figure 9.
| Variable | Dimension 1 | Dimension 2 |
|---|---|---|
| Increase in private vehicles per dwelling unit | 1.5043 | 0.499 |
| changes in household income | -1.1314 | 0.2415 |
| Online-based technology needs | -0.0628 | 1.617 |
| improved accessibility of the area | 0.2485 | -0.9261 |
| lifestyle change | -1.1258 | 0.2815 |
| individual mobility | 1.6157 | -0.6463 |
| Socio-economic space interactions | 1.5043 | 0.499 |
In Figure 8, point a proximal to IP was used to find the order of preference among the points represented. This included lifestyle changes, improved accessibility of the area, socio-economic spatial interactions, changes in household income, online-based technology needs, increase in private vehicles, and individual mobility. Lifestyle changes and an increase in private vehicles per dwelling unit had the highest and lowest preference. In addition, perceptual mapping conditions showed the reality in community resulting in the following scenario:
Scenario IP type 1 Increase in private vehicles per dwelling unit, improved accessibility of the area, individual mobility.
Scenario IP type 2 Increase in private vehicles per dwelling unit, changes in household income, and lifestyle, as well as socio-economic spatial interactions.
In IP scenario, a scale of preferences can be generated by identifying the ideal or priority points. Scenario IP. 1, the increase in private vehicles had been the main driver of the improvement in accessibility, prompting socio-economic interactions. Furthermore, IP scenario type 2 proved that the increase in private vehicles was also significantly drove the rise in area accessibility. These two variables led to the formation of the final destination, thereby prompting the socio-economic spatial interaction of BTP settlement area.
Based on the ideal perception point maps of scenarios 1 and 2, the increase in private vehicles per dwelling unit was regarded as the main material need of community that contributes to the formation of cultural diffusion through lifestyle changes and individual mobility for social and economic spatial interaction. The perception of people in both direct and indirect interacting spaces was determined by the main material needs in mobility, which relied on the culture of using private vehicles and online-based transportation, such as Gojek, Grab, and Maxim.
Individual mobility is an integrated flow of aspects including socio and economic needs. Mobility habits are influenced by changes in lifestyle, increased ownership of private vehicles, household income, and use of technology. In BTP settlement area, the management of urban transport system has not been able to implement a collective mobility method, as proven by the relationship between increased private vehicle ownership and online application businesses, regarded as a dual-purpose concept. The direction of urban policy development and transport systems in Indonesia remains individualistic mobility, while interaction patterns continue to form reciprocal relationships based on social and economic factors including suburban community culture.
The following analysis focused on the development and integration flow processes of socio-economic and cultural diffusion, including information technology tending toward the shifting phase of individual mobility in spatial interaction.
The development of suburban areas was due to socio-economic and lifestyle changes. This had implications for transport policy and materialistic values, with increased number of private vehicles, and accessibility, changes in income, including the need for online-based technology as determining factors influencing varying lifestyles. Furthermore, increased number of private vehicles, regional accessibility, and lifestyle changes were regarded as determining factors that influenced individual mobility. This was marked by the selection of dominant private vehicle modes, including changes in household economic income and lifestyle in relation to daily activities and interaction conducted both internally and externally in suburban areas of Makassar City.
Community perception focused on two scenarios, the first was an increase in private vehicles regarded as the main driver for realizing individual mobility. The second scenario was prompted by the rise in private vehicle ownership in relation to socio-economic spatial interaction in suburban settlement areas of Makassar City. This perception model proved that the increased number of private vehicles, changes in income, the need for online-based technology, accessibility, lifestyle, and individual mobility were series of dynamic processes leading to the main aim of realizing socio-economic spatial interaction. Practically, the results obtained was aimed to improve the substantive concept of integration and adopted methods based on estimating the generation and distribution of movements without ascertaining the process and ability of real travelers, making it difficult for the level of accuracy and usefulness to be adopted. These two results led to the development and integration flow processes of socio-economic and cultural diffusion including information technology, which tended towards the shifting phase of individual mobility in spatial interaction. Furthermore, it could be used to enact urban development and transport sector policies to realize sustainable suburban growth.
Conceptualization, M.M. and E.A.; methodology, E.A.; software, E.A.; data collection, E.A. and K.A.; resources, M.M. and E.A.; data curation, E.A. and S.; writing—original draft preparation, M.M., E.A.; writing—review and editing, E.A.; supervision, M.M. and E.A. All authors have read and agreed to the published version of the manuscript.
The authors are grateful to Makassar City Government and Perumnas Developers for the data collection process and Bosowa University for prompting this research process.
