Excess Commuting and the Modifiable Areal Unit Problem

Horner, Mark W.; Murray, Alan T.

doi:10.1080/00420980220099113

Cited by 176 publications

(134 citation statements)

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“…Depending on the decision, substantially different conclusions can be drawn. However, in an investigation of the scale dependence of three measures of commuting efficiency, Niedzielski, Horner, and Xiao (2013) found that measures of capacity used and commuting economy were relatively unaffected by the areal unit, though a measure of excess commuting was highly sensitive to modifiable areal unit problems, confirming earlier findings (Horner & Murray, 2002). The authors concluded, though, that "more aggregated data, such as LEHD data aggregated to census tracts for example, can be used safely in the knowledge that the metric results will hardly be different from those based on less aggregated data" (Niedzielski et al, 2013, p. 141).…”

Section: Geographic Scale and Metric Calculationsupporting

confidence: 59%

Low-wage jobs-housing fit: identifying locations of affordable housing shortages

Benner

Karner

2016

Urban Geography

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Finding the right jobs-housing balance has long been an important concern for urban planners. More recently, attention has turned to jobs-housing fit -the extent to which housing price is well matched to local job quality. Prior analyses have been constrained by a lack of local data on job quality, making it difficult to identify the geography and scale of the problem. We introduce a new methodology for calculating the low-wage jobs-housing fit at both a jurisdiction and neighborhood scale that was designed in collaboration with affordable housing advocates and has been directly applied in urban planning and affordable housing policy efforts. Low-wage fit is particularly important because of ongoing difficulties with affordable housing provision and the disproportionate benefits of reducing transportation costs for low-income earners. We use the calculated metric at both a city and neighborhood scale to identify what can be learned from a low-wage jobshousing fit metric that is not evident in traditional measures of jobs-housing balance. In contrast to jobs-housing balance, the low-wage fit analysis clearly highlights those jurisdictions and neighborhoods where there is a substantial shortage of affordable housing in relation to the number of low-wage jobs. Because of the geographic coverage of the data sources used, the results can be widely applied across the United States by affordable housing advocates, land-use planners, and policy makers. ARTICLE HISTORY

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Section: Geographic Scale and Metric Calculationsupporting

confidence: 59%

Low-wage jobs-housing fit: identifying locations of affordable housing shortages

Benner

Karner

2016

Urban Geography

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“…In every case where data is simulated or deemed uncertain, this must be taken into account when interpreting the results, a critical point in any situation where environmental policies are based on model data (Smil, 1993). In addition, because only one level of administrative zones is used in this study, the results could be susceptible to the modifiable areal unit problem (Openshaw, 1983;Horner and Murray, 2002), although the individuallevel metrics should be more robust to this effect than aggregate-level measures. Validation techniques and sensitivity analyses should be undertaken before policies based on the vulnerability metrics are implemented (Edwards and Clarke, 2009).…”

Section: Discussionmentioning

confidence: 99%

The ‘oil vulnerability’ of commuter patterns: A case study from Yorkshire and the Humber, UK

Lovelace

Philips

2014

Geoforum

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Motorised transport infrastructure and increasingly specialised labour markets have resulted in energy intensive commuter patterns in many parts of the world. This is cause for concern due to the possibility of oil price shocks and the need to restrict the combustion of fossil fuels to minimise the chances of runaway climate change. The paper investigates methods to identify the spatial distribution and socio-demographic profiles of those who are vulnerable to high oil prices. It does this by use of four metrics of oil vulnerability which were developed using a spatial microsimulation model and applied to the case study region of Yorkshire and the Humber, UK. The metrics capture different aspects of vulnerability and highlight the importance of translating conceptual definitions into practical metrics. The geographically aggregated results coincide with the literature: rural areas are associated with the highest levels of vulnerability. The individual level results indicate that vulnerability can be prevalent even in seemingly resilient areas. Ultimately, we conclude that the social and spatial distribution of oil vulnerability depends on how an energy-constrained future is envisioned. Creating localised metrics about the future is a challenging task fraught with danger but could, provided that these metrics are interpreted with sufficient humility, aid the development of equitable policies to encourage resilience, as part of a worldwide transition away from fossil fuels.

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“…As such, geographic scale selection has reflected a subjective exercise of the researcher seeking to aggregate non-modifiable entities (Openshaw, 1983). Since the MAUP has been defined as a dilemma related to the selection of geographic scale and analytical unit, any evaluation of travel behavior must give careful consideration to these facets (Horner & Murray, 2002). However, disparity in the geographic scales previously used within the literature have complicated the debate of how best to operationalize land use mix for active travel research (Clark & Scott, 2014).…”

Section: Methodological Concernsmentioning

confidence: 99%

Land Use Mix and Pedestrian Travel Behavior: Advancements in Conceptualization and Measurement

Gehrke¹

2000

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Smart growth policies have often emphasized the importance of land use mix as an intervention beholding of lasting urban planning and public health benefits. Past transportation-land use research has identified potential efficiency gains achieved by mixed-use neighborhoods and the subsequent shortening of trip lengths; whereas, public health research has accredited increased land use mixing as an effective policy for facilitating greater physical activity. However, despite the celebrated transportation, land use, and health benefits of improved land use mixing and the extent of topical attention, no consensus has been reached regarding the conceptualization and measurement of this key smart growth principle or the magnitude of its link to walking.This dissertation, comprised of three empirical studies, explores this topic in detail.In the first study, activity-based transportation and landscape ecology theory contributed to the introduction of a multifaceted land use mix construct reflected by a set of composition and configuration indicators. This activity-related land use mix construct, and not the commonly used entropy index, was a significant built environmental determinant of walk mode choice and home-based walk trip frequency.In the second study, structural equation modeling was used to establish a connection between residing in a smart growth neighborhood and home-based pedestrian travel.This study discovered a multidimensional depiction of the traveler's residential environment that was reflective of local land use mix, employment concentration, and pedestrian-oriented design. The second-order factor, which described a smart growth ii neighborhood, had a strong and positive effect on the household-level decision to walk for transportation-related and discretionary travel when assessed in a multidirectional conceptual framework.In the final study, the influence of geographic scale selection on the connection between the built environment and active and auto-related travel was explored.Informed by this sensitivity analysis, which underlined the existence of scaling and zoning effects, mode choice for both work and nonwork travel as a function of individual, household, transportation, and built environment features at the home location and destination was modeled. These discrete choice analysis results found that measures of land use mix and density at each trip end had the strongest effect on the decision to walk rather drive or ride in a vehicle for nonwork trips. In all, the findings from this dissertation provide policymakers and practitioners greater specificity in the measurement of land use mix and its connection to pedestrian travel behavior.iii Acknowledgments

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Excess Commuting and the Modifiable Areal Unit Problem

Cited by 176 publications

References 15 publications

Low-wage jobs-housing fit: identifying locations of affordable housing shortages

Low-wage jobs-housing fit: identifying locations of affordable housing shortages

The ‘oil vulnerability’ of commuter patterns: A case study from Yorkshire and the Humber, UK

Land Use Mix and Pedestrian Travel Behavior: Advancements in Conceptualization and Measurement

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