Modelling the spatial interactions between workplace and residential location

Ibeas, Ángel; Cordera, Rubén; dell’Olio, Luigi; Coppola, Pierluigi

doi:10.1016/j.tra.2013.01.008

Cited by 36 publications

(27 citation statements)

References 29 publications

(31 reference statements)

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“…However, if changes to the job situation are not significant enough, the household may not initiate the relocation process. Similar models are presented in studies on land-use and residential mobility (Kan (1999), Vega and ReynoldsFeighan (2009), Pagliara and Wilson (2010), Ibeas et al (2013), Jun (2013)). …”

Section: Introductionmentioning

confidence: 81%

A data-driven predictive model for residential mobility in Australia – A generalised linear mixed model for repeated measured binary data

Namazi-Rad

Mokhtarian²,

Shukla

et al. 2016

Journal of Choice Modelling

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Household relocation modelling is an integral part of the Government planning process as residential movements influence the demand for community facilities and services. This study will address the problem of modelling residential relocation choice by estimating a logit-link class model. The proposed model estimates the probability of an event which triggers household relocation. The attributes considered in this study are: requirement for bedrooms, employment status, income status, household characteristics, and tenure (i.e. duration living at the current location). Accurate prediction of household relocations for population units should rely on real world observations. In this study, a longitudinal survey data gathered in the Household, Income and Labour Dynamics in Australia (HILDA) program is used for modelling purposes. The HILDA dataset includes socio-demographic information such general health situation and well-being, lifestyle changes, residential mobility, income and welfare dynamics, and labour market dynamics collected from the sampled individuals and households. The technique presented in this paper links possible changes in households' socio-demographic characteristics to the probability of residential relocation by developing a mixed effects discrete-choice logit model (MEDCLM) for longitudinal binary data using the HILDA dataset. The proposed model captures the effect of repeated measurements together with the area-specific random effects. AbstractHousehold relocation modelling is an integral part of the Government planning process as residential movements influence the demand for community facilities and services. This study will address the problem of modelling residential relocation choice by estimating a logit-link class model. The proposed model estimates the probability of an event which triggers household relocation. The attributes considered in this study are: requirement for bedrooms, employment status, income status, household characteristics, and tenure (i.e. duration living at the current location). Accurate prediction of household relocations for population units should rely on real world observations. In this study, a longitudinal survey data gathered in the Household, Income and Labour Dynamics in Australia (HILDA) program is used for modelling purposes. The HILDA dataset includes socio-demographic information such general health situation and wellbeing, lifestyle changes, residential mobility, income and welfare dynamics, and labour market dynamics collected from the sampled individuals and households. The technique presented in this paper links possible changes in households' socio-demographic characteristics to the probability of residential relocation by developing a mixed effects discrete-choice logit model (MEDCLM) for longitudinal binary data using the HILDA dataset. The proposed model captures the effect of repeated measurements together with the area-specific random effects.

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Section: Introductionmentioning

confidence: 81%

A data-driven predictive model for residential mobility in Australia – A generalised linear mixed model for repeated measured binary data

Namazi-Rad

Mokhtarian²,

Shukla

et al. 2016

Journal of Choice Modelling

View full text Add to dashboard Cite

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“…An important aspect of future work will be to calibrate the model using past land-cover data (e.g. Ibeas et al 2013). relationship between land use and land cover, see Verburg (2006).…”

Section: Discussionmentioning

confidence: 99%

“…6. Ibeas et al (2013) attempted to model the spatial interactions between workplace and residential location while their model is based on more aggregated zones. 7.…”

Section: Discussionmentioning

confidence: 99%

Creation of future urban environmental scenarios using a geographically explicit land-use model: a case study of Tokyo

2013

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In the present study, a large-scale geographically explicit land-use model was developed for projecting the geographical distribution of urban environmental variables, such as population density and fraction of urban and green vegetation land cover, for different urban forms. These variables form key inputs for regional climate models, yet they are sometimes addressed in an ad hoc manner. This study employs a land-use equilibrium model based on urban economic theory, which endogenously projects the geographical distribution of households, residential floor space/rent and land area/rent. The model can deal with not only urban growth but also urban shrinkage, which is becoming an important issue for developed countries, including Japan, confronting population decrease. The model is calibrated for the Tokyo Metropolitan Area at the micro-district level. Using the model, this paper demonstrates an extreme urban compact city scenario for the year 2050, and it is compared to the business as usual (BAU) scenario.

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“…Existing research on food access used mainly a few sociodemographic variables such as education, race/ethnicity, and income, as addressed early in this section (Nie and Zepeda, 2011; Bravo et al, 2013; Lea and Worsley, 2005). Our variables also included population density (Ibeas et al 2013), age, housing value (Dong 2015), and employment status (Greenwood 1975), which are other important measures of demographics and socioeconomic statuses used in the demography literature (e.g., Chi 2009). The current study also employed a set of innovative and comprehensive measures of real-time accessibility.…”

Section: Introductionmentioning

confidence: 99%

Sociodemographics and access to organic and local food: A case study of New Orleans, Louisiana

Ghiasi

et al. 2018

Cities

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This study examined the association between physical accessibility to organic and local food, and sociodemographic factors in New Orleans, Louisiana. Spatial regression models were used to investigate how sociodemographic variables such as income, race/ethnicity, education, and age correlate with driving, bicycling, and walking distances to stores that sell organic or local food. The distances were calculated from GIS and real-time speed information from Google Maps. The results indicated that physical access to such stores is positively associated with population density, median housing value, education, non-Hispanic Blacks, and Hispanics, and is negatively associated with median housing age. We found no disparities in access to organic and local food on the basis of income and race.

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Modelling the spatial interactions between workplace and residential location

Cited by 36 publications

References 29 publications

A data-driven predictive model for residential mobility in Australia – A generalised linear mixed model for repeated measured binary data

A data-driven predictive model for residential mobility in Australia – A generalised linear mixed model for repeated measured binary data

Creation of future urban environmental scenarios using a geographically explicit land-use model: a case study of Tokyo

Sociodemographics and access to organic and local food: A case study of New Orleans, Louisiana

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