Change In Residential Location Research Articles

Assessing the Impacts of Autonomous Vehicles on Urban Sprawl

Background: Urban sprawl adversely effects the sustainability of urban environments by promoting private vehicle use, decreasing the viability of active/public transport, and increasing the cost of public service provision. Autonomous vehicles could change the desirability of different residential locations due to resulting changes to urban design and decreased value of travel time. Methods: Adult Australians (n = 1078) completed an online survey that included a description of a future where autonomous vehicles are widely available. The respondents reported anticipated changes in residential location in this autonomous future. Frequency analyses were conducted, and three logistic generalised linear models were run to identify factors associated with staying in the same area or moving to higher- or lower-density locations. Results: Autonomous vehicles are likely to have mixed effects on people’s desired residential locations. Most respondents (84%) elected not to move location, 11% intended to move to lower-density locations, and 6% to higher density locations. Reasons for moving included a desire for more space, the ease of travelling in urban areas, and the reduced value of travel time. Conclusion: The introduction of autonomous vehicles will need to be managed to avoid fostering increased urban sprawl and the associated negative consequences. Strategies that increase the liveability of higher density urban environments are likely to discourage urban sprawl in a future characterised by autonomous transport options.

Dynamics in residential relocation, car ownership, and carsharing adoption in neighborhoods with a high prevalence of carsharing

Many studies have looked into the private car ownership and residential environment factors that affect current carsharing adoption from a cross-sectional perspective, while very few have focused on dynamic variables related to home relocation and changes in vehicle access. Using survey data collected in eight typical neighborhoods with a high level of carsharing in Utrecht, the Netherlands, this study explored how dynamics in car ownership, residential relocation, and current carsharing membership are related, and how this relationship shapes intention for future carsharing membership. The explanatory variables were the changes in residential location, and past and expected changes in private car ownership. Results of our binary logistic regression and ordinary least squares regression modeling show that residential relocation has a limited effect on carsharing adoption: Newcomers from lower urbanization level neighborhoods need some time to get used to carsharing and adopt it. For car ownership dynamics, future intentions to change private car ownership are more likely to affect current and future carsharing adoption than previous changes: Previous and future changes in car ownership and carsharing adoption have different effects on actual and expected behaviors. Our study shows that different functional goals of carsharing services for different types of potential users should be distinguished.

CAUSING FACTORS OF MIGRATION AND HUMAN RIGHTS

The said article on the study of the migration process of the motivation and push factors for migration and analysis of the human rights of the people, the cause of the migration of one state party can be the cause of human rights as the citizens of the country who make the behavior as a decision and on the other hand, it is very important for the migrants. There are many contributing factors to migration, including economic, political, socio-political, human rights factors, natural, health-related, cultural and educational factors. States where the level of social and economic life is low, human rights are not protected, the citizen has no income, there are small and unpromising opportunities for employment, due to which the unstable political environment loses a large part of citizens over time, therefore migration can be due to both labor and family factors. The article also discusses the facts of migration and the directions caused by political factors and coldblooded confrontations in the past, the First and Second World Wars led to the largest-scale migrations and changes in residential location. Also important is the structure and nature of migration, which can be shaped by the causes of violence and war. In international migration, human rights are particularly relevant and important, systemic or legal problems before leaving the country, and the attitudes of migrants towards them in the receiving state, are their rights fully protected? The important research results in the article are: Migration has acquired its character based on historical facts from the past, for example population migration is frequent from East Africa to North Africa, from East Africa to Europe, as well as population migration from Islamic states to Europe due to the insecurity of the population, especially women’s rights, with the motive of providing physical protection and security. The desire to get an education and simply to travel remains a contributing factor to migration among young people. The factors causing migration in international migration remain: the right to life, the right to personal freedom, the right to religious freedom, freedom of movement and movement, freedom of speech, the right to physical security. Key words: migration, challenge, human, right.

Yearly Development of Car Ownership in Urban and Rural Environments

The decision to own a car usually results from a mix of changes in socio-economics, residential location, demographics or preferences of the individuals. These changes do not directly influence car ownership but are lagged in time or sometimes occur on anticipation of future events. In this research we investigate how some of the most common predictors for car ownership change over the years and how much impact they actually have in their decision for owning a car. We make a distinction in impact for people living in an urban and people living in a rural environment. A multinomial logit was estimated in 4 separate years to assess the impact of changes in residential location, age and income. The results show that not only the impact of these common predictors is changing but also the relation that they have regarding car ownership. These results illustrates the complexity of car ownership and suggests a more dynamic approach for the prediction of car ownership in both urban and rural environments.

The Association Between Retiree Migration and Retirement Satisfaction

The purpose of this study is to examine migration during retirement and its association with retirement satisfaction. Utilizing longitudinal data collected from the Health and Retirement Study, this study estimates a fixed-effects logit model to examine how changing U.S. Census divisions during retirement is related to retirement satisfaction. The findings suggest that a change in residential location during retirement is associated with an increase in retirement satisfaction. In planning for retirement, individuals should examine what will provide them with the highest level of satisfaction during their retirement and whether their current location can facilitate an enjoyable retirement. Financial planners and counselors should also consider, as a part of their systemic retirement planning process, increasing the attention that is given to the residential location in which their clients will reside during retirement.

The impact of land use effects in infrastructure appraisal

When benefits of proposed infrastructure investments are forecasted, residential location is usually treated as fixed, since very few operational transport models are able to forecast residential relocation. It has been argued that this may constitute a source of serious error or bias when evaluating and comparing the benefits of proposed infrastructure investments. We use a stylized simulation model of a metropolitan region to compare calculated benefits for a large number of infrastructure investments with and without taking changes in residential location into account. In particular, we explore the changes in project selection when assembling an optimal project portfolio under a budget constraint. The simulation model includes endogenous land prices and demand for residential land, heterogeneous preferences and wage offers across residents, and spillover mechanisms which affect wage rates in zones. The model is calibrated to generate realistic travel patterns and demand elasticities. Our results indicate that ignoring residential relocation has a small but appreciable effect on the selected project portfolio, but only a very small effect on achieved total benefits.

Changes in inequality in utilization of preventive care services: evidence on China\u2019s 2009 and 2015 health system reform

BackgroundEnsuring equal access to preventive care has always been given a priority in health system throughout world. This study aimed to decompose inequality in utilization of preventive care services into its contributing factors and then explore its changes over the period of China’s 2009–2015 health system reform.MethodsThe concentration index (CI) and decomposition of the CI was performed to capture income-related inequalities in preventive services utilization and identify contribution of various determinants to such inequality using data from China Health and Nutrition Survey. Then, changes in inequality from 2009 to 2015 were estimated using Oaxaca-type decomposition technique.ResultsThe CI for preventive services utilization dropped from 0.2240 in 2009 to 0.1825 in 2015. Residential location and household income made the biggest contributions to income-related inequalities in these two years. Oaxaca decomposition revealed changes in residential location, regions and medical insurance made positive contributions to decline in inequality. However, alternation in household income, age and medical services utilization pushed the equality toward deterioration.ConclusionThe pro-rich inequality in preventive healthcare services usage is evident in China despite a certain decline in such inequality during observation period. Policy actions on eliminating urban-rural and income disparity should be given the priority to equalize preventive healthcare.

Qualitatively exploring the effect of change in the residential environment on travel behaviour

Qualitative research with residents relocating from London was undertaken to develop an understanding of how and to what extent a change in the residential environment affected people’s travel behaviour and attitudes. Data was collected through semi-structured interviews and was thematically analysed. The findings reveal that when in a new location, residents observe the features of their built environment, identify the cause of their stress, and make efforts to address it with a change in travel behaviour. The key contribution of this study is the realisation of different levels of travel behaviour in response to a change in residential location – some residents maintained their travel behaviour, some complemented it, while some changed their behaviour to adapt to their new built environment. Theoretically, this research contributes to the extension of knowledge on travel behaviour as it focuses on suburbanising Londoners; the qualitative method adopted for this research also contributes to current knowledge. Practically, there is the potential of developing a travel behaviour change initiative around ridesharing and policy changes and initiatives to improve physical planning and sustainable travel.

Urban developments and daily travel distances: Fixed, random and hybrid effects models using a Dutch pseudo-panel over three decades

As people require time to adjust their travel behaviour to changes in residential location and transport infrastructure, there is a need for long-term empirical studies quantifying the relationships between locations, individuals and travel behaviour. Such empirical evidence is critical for assessing previous and candidate future land use-transport policies. Existing research however, has mostly investigated travel behaviour during relatively short time periods and for a single transport mode. This paper examines the development of travel behaviour and its socio-demographic and location determinants, using Dutch National Travel Survey data from 1980 to 2010 among other sources, for the Randstad, the Netherlands. A pseudo panel analysis is conducted to investigate the effect of various indicators on average daily distance travelled by train, car and bicycle over three decades. Econometric models including pooled ordinary least squares, fixed and random effects and a hybrid model were tested to identify the best fit. The results indicate that average daily distance travelled rose until the mid-1990s before witnessing a decrease till 2010. Interestingly, half of the Randstad inhabitants have been travelling ≤26 km per day over the past thirty years. Furthermore, as people grow older, they increasingly travel more by train and bicycle. Finally, a rise in suburban inhabitants decreases the average distance travelled by train and increases that of bicycle, while a rise in rural inhabitants encourages higher distances travelled by car.

The role of parents’ mobility behavior for dynamics in car availability and commute mode use

Recent studies have analyzed travel behavior over the life course through the lens of the mobility biography approach. Similarities in the effects of certain key events on travel behavior have been found. At the same time, family background influences changes in travel behavior in the long run. However, to date, the role of parents’ mobility behavior on their adult children’s mobility behavior has not yet been analyzed. In addition, there has been no empirical analysis of mobility biographies that simultaneously considers aging, key events, changes in residential location, changes in commute distance and parental socialization. This paper uses intergenerational mobility biographies and investigates the role of these variables in explaining changes in car availability and commuting mode use. To this end, a unique retrospective dataset is used of more than 1600 mobility biographies of persons born around 1957 as well as the mobility biographies of their parents. Markov models are used to analyze mobility behavior over the life course. The results show that parents’ behavior is not directly associated with the behavior of their adult descendants. However, age, attitude towards the car, gender and changes in the built environment are important predictors for dynamics in car availability. Commute mode changes are dominantly associated with changes in commute distance but also with changes in car availability and attitude. Key events such as divorce or childbirth are less important. The results suggest that there are indirect parental socialization effects via residential location and attitude on car availability and commute mode use. This paper points out the importance of the life course, changes in commute distance and changes in the built environment for car availability and commute mode use and is an example of the application of Markov models for the analysis of quantitative mobility biographies.

Moving on: How depressive symptoms, social support, and health behaviors predict residential mobility.

There are major health inequalities between residential areas. However, it remains unclear whether these inequalities are due to social causation or selective residential mobility, because little is known about the associations between health-related factors and selective residential mobility. This study examined how depressive symptoms, social support, and health behaviors are associated with subsequent residential mobility, as measured by frequency, distance, and direction of moves. Participants were selected from the Young Finns prospective cohort study (N = 3017) with four study waves in 1992, 1997, 2001, and 2007. Complete residential mobility history was gathered for each participant from registry data. Residential mobility was assessed over three-year periods following each study wave. The direction of mobility was measured as changes in residential location's population density, health index, mortality index, and unemployment. Area characteristics were used as covariates together with the participants' age, sex, and education. Individuals reporting higher social support from friends were more likely to move (b = 0.20; 95% confidence interval (CI): 0.08, 0.37) and move more frequently (b = 0.08; 95% CI: 0.01, 0.14), whereas individuals reporting higher social support from their family members were less likely to move (b = -0.08; 95% CI: -0.14, -0.02). Better health behaviors were associated with longer moving distances (b = 0.14; 95% CI: 0.06, -0.23). None of the individual characteristics were associated with the direction of moves as measured by changes in municipality characteristics. CONCLUSIONS SOME OF THE HEALTH-RELATED CHARACTERISTICS ARE ASSOCIATED WITH RESIDENTIAL MOBILITY IN FINLAND HOWEVER, THESE INDIVIDUAL CHARACTERISTICS DO NOT SEEM TO PREDICT SYSTEMATIC SELECTIVE RESIDENTIAL MOBILITY ACROSS MUNICIPALITIES WITH DIFFERENT REGIONAL HEALTH PROFILES.

Visually-Driven Urban Simulation: Exploring Fast and Slow Change in Residential Location

A large-scale residential-location model of the Greater London region is being developed in which all stages of the model-building process—from data input, analysis through calibration to prediction—are rapid to execute and accessible in a visual and immediate fashion. The model is structured to distribute trips across competing modes of transport from employment to population locations. It is cast in an entropy-maximising framework which has been extended to measure actual components of energy—travel costs, free energy, and unusable energy (entropy itself)—and these provide indicators for examining future scenarios based on changing the costs of travel in the metro region. Although the model is comparatively static, we interpret its predictions in terms of fast and slow processes—‘fast’ relating to changes in transport modes, and ‘slow’ relating to changes in location. After developing and explaining the model using appropriate visual analytics, a scenario in which road-travel costs double is tested: this shows that mode switching is considerably more significant than shifts in location—which are minimal.

A Dynamic Model of Commutes

This paper studies the interaction between commuting, job mobility, and housing mobility. Many conventional models assume that the employment location has priority over the residential location and that the latter is adapted to the former. This implies that commutes which start with a job change will often be short lived because of a change in residential location that soon follows. It is also often supposed that the change in residential location is made with the intention to avoid long commutes. In this paper we test the empirical validity of these hypotheses. Our data are a sample of Dutch workers who report changes on the housing and labor market between 1990 and 1998. It appears from these data that both job mobility and housing mobility are often followed by repeat mobility on the same market, but also on the other market. Job mobility indeed triggers residential mobility, but the effect of residential mobility on job changes is of comparable magnitude. Moreover, both types of mobility lead to substantial repeat mobility. We specify duration models that focus on the time during which employment–housing arrangements (hence, commutes) remain unchanged. Estimation results for these models confirm that commutes which start with housing mobility and those which start with job mobility have similar characteristics with respect to induced future mobility. We are unable to find evidence supporting the hypothesis that long commutes resulting from a job change induce additional residential mobility. Another result of the analysis is that workers belonging to dual-earner households are more mobile on both markets than other workers.

Modeling the Impact of Key Events on Long-Term Transport Mode Choice Decisions: Decision Network Approach Using Event History Data

This paper describes the first phase of a study of the impact of key events on long-term transport mode choice decisions. The suggested complexity of transport mode choice is modeled with a Bayesian decision network. An Internet-based questionnaire was designed to measure the various conditional probability tables and the conditional utility tables of the Bayesian decision network. Seven key events were implemented in the questionnaire: change in residential location, change in household composition, change in work location, change in study location, change in car availability, change in availability of public transport pass, and change in household income. Data from 554 respondents were used to illustrate how the tables can be constructed on the basis of event history data.

Modeling the Impact of Key Events on Long-Term Transport Mode Choice Decisions

This paper describes the first phase of a study of the impact of key events on long-term transport mode choice decisions. The suggested complexity of transport mode choice is modeled with a Bayesian decision network. An Internet-based questionnaire was designed to measure the various conditional probability tables and the conditional utility tables of the Bayesian decision network. Seven key events were implemented in the questionnaire: change in residential location, change in household composition, change in work location, change in study location, change in car availability, change in availability of public transport pass, and change in household income. Data from 554 respondents were used to illustrate how the tables can be constructed on the basis of event history data.

The influence of a work-oriented life style on residential location choice of couples

Numerically, the traditional family, with the man as breadwinner and the woman as homemaker, has decreased in importance. The dominant force behind this process is individualization and growth of real income (Lesthaeghe and Meekers, 1986). It went hand in hand with lower fertility, higher divorce rates, people staying single for a longer time, and growing female labour force participation. A major consequence of this process is the change in residential location needs. For instance, one of the triggers of gentrification is the rise of single households and childless dual-career households (Lipton, 1977; Hamnett, 1984; Dengschat, 1991). In gender studies it is often argued that suburban 2 environments are relatively unfriendly to working women. Most suburban areas were built in a period in which the dominant view prescribed a sharp division of labour in the household: the husband doing paid work elsewhere, the woman doing unpaid work at home. In short, this view led to the creation of suburbs, characterized by long commuting distances from the city center (where most employment was located), poor public transportation, and limited child-care facilities. A contradiction has grown between the changing gender relations and women's changing roles on the one hand and, on the other, the present form of the suburbs (England, 1991). Therefore, central cities are regarded as better environments for women compared with single-familydwelling suburbs (Rose, 1984). In cities, women have more access to facilities and a greater diversity of housing and services. According to Stapleton (1980), it is the changing allocation of time between domestic labour, paid labour, and leisure which leads to alterations in location needs. The fact that working women have better opportunities in central cities, and for that reason can be found more frequently in these environments, is not empirically corroborated in the Netherlands. There, many dual-career households are living in suburban areas (Kruythoff, 1993; Camstra, 1994). Factors which seem to influence residential environment choice are the equality of hours worked, level of education, having children, income, and stage in the life

Urban Violence and Residential Mobility

Abstract Data from a recently completed national longitudinal survey suggest, contrary to popular expectations, that individual perceptions of local violence have at best only a very moderate influence on significant changes in residential location, that concern with crime problems does not seem to result in a major exodus to the suburbs, and that what little effect urban crime has on mobility is stronger for the poor and black than for high and middle income whites. The findings are interpreted to indicate that those groups who are most affected by crime and violence, the poor and the black, are precisely those groups least able to escape the problem through residential relocation.