Modeling the Effect of Land Use on Activity Spaces

Harding, Chris; Patterson, Zachary; Miranda-Moreno, Luis F.; Zahabi, Seyed Amir H

doi:10.3141/2323-08

Cited by 29 publications

(24 citation statements)

References 26 publications

(59 reference statements)

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“…Mirroring results from Harding et al (2012), 'homemakers', a CT variable that represents the percentage of women at home who perform 15 hours or more of unpaid child care, was positive and significant. Many of these high homemaker-value tracts are both affluent and some distance from employment centers.…”

Section: Linear Regression and Simultaneous Equation Modelsmentioning

confidence: 90%

“…Building upon previous work conducted using a subset of the land-use variables and data for the Montreal region alone (Harding et al, 2012), traditional measures such as population and employment densities, land-use mix, and public-transit accessibility are clustered to create a neighborhood typology for each of the cities. Activity space polygons are then generated from two distinct origin-destination (OD) surveys for Quebec City, three for Montreal, and one for Sherbrooke.…”

Section: Introductionmentioning

confidence: 99%

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A Spatial and Temporal Comparative Analysis of the Effects of Land-Use Clusters on Activity Spaces in Three Quebec Cities

Harding

Patterson

Miranda-Moreno

et al. 2014

Environ Plann B Plann Des

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Previous literature on transportation and land use has focused on the effect of individual land-use variables, such as population and employment density, and on measures of transportation demand, such as vehicle kilometers traveled and mode split. In contrast, our work uses activity spaces, a relatively unexplored measure of travel dispersal, as a dependent variable and neighborhood clusters to capture the effect of land use on this variable. This paper is an extension of previous research that dealt with Montreal exclusively and similar methods are used to compare three cities (Montreal, Quebec City, and Sherbrooke) over multiple years (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008). We control and tests for the possibility of residential location self-selection bias through simultaneous equation modeling. The main findings are that (i) activity spaces are clearly linked to land use (through neighborhood clusters), as well as to overall city size; (ii) activity spaces appear to be growing over time where employment centers are fixed; and (iii) exogeneity in explanatory variables cannot be rejected.

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Section: Linear Regression and Simultaneous Equation Modelsmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

A Spatial and Temporal Comparative Analysis of the Effects of Land-Use Clusters on Activity Spaces in Three Quebec Cities

Harding

Patterson

Miranda-Moreno

et al. 2014

Environ Plann B Plann Des

Self Cite

View full text Add to dashboard Cite

show abstract

“…(13), and L α (D) be the α-level set associated with r(D) as in Eq. (16). Given a level α ∈ [0, 1] and a stability threshold γ > 0, we define the last crossing time of the sequence of level sets {L α (D) : D = 1, .…”

Section: 3mentioning

confidence: 99%

A statistical framework for measuring the temporal stability of human mobility patterns

Dong

Chen

Dobra

2020

Journal of Applied Statistics

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Despite the growing popularity of human mobility studies that collect GPS location data, the problem of determining the minimum required length of GPS monitoring has not been addressed in the current statistical literature. In this paper we tackle this problem by laying out a theoretical framework for assessing the temporal stability of human mobility based on GPS location data. We define several measures of the temporal dynamics of human spatiotemporal trajectories based on the average velocity process, and on activity distributions in a spatial observation window. We demonstrate the use of our methods with data that comprise the GPS locations of 185 individuals over the course of 18 months. Our empirical results suggest that GPS monitoring should be performed over periods of time that are significantly longer than what has been previously suggested. Furthermore, we argue that GPS study designs should take into account demographic groups.

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“…This intermediate scale is where the majority of daily routines, behaviors, and lifestyle decisions are made [60], a notion which has been termed 'activity space' in previous geography and environmental psychology studies [60][61][62]. The size of this space varies between individuals, but previous studies using GPS trackers and travel diaries have measured it to range from 10 to 80 sq km [63][64][65], an area larger than three Central Parks but smaller than the borough of Manhattan, New York. The human decisions based on this space shape multiple aspects of sustainability, and yet, this sub-metropolitan scale is currently unintelligible in most current urban classification systems.…”

Section: Nomenclature To Characterize Urban Landscapesmentioning

confidence: 99%

Characterizing and measuring urban landscapes for sustainability

Stokes

Seto

2019

Environ. Res. Lett.

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Urban areas are key to sustainability, and understanding heterogeneity in urban landscapes is important for linking development patterns to ecological, economic, and social health. Here, we characterize the urban landscape for the purpose of revealing structural variations that affect sustainability. We develop a new language and classification schema for breaking down urban areas into sub-metropolitan land units that, unlike administrative boundaries, are based on objective measures of the built and natural environment and are comparable across and within urban areas. These units capture structural differences that population density does not. The classification schema offers a process-based characterization of urban landscapes-one where 'urban' is defined by the human and biophysical interactions mediated by the urban environment and complements existing land classification systems, like those based on land use and land cover. As an example, the schema is applied here to understand transportation behaviors-a particular urban process with wide-ranging implications for urban sustainability. Using GIS, satellite, and census spatial data, we apply the classification schema in 909 US urban areas, systematically clustering development with similar structural attributes linked to transportation behaviors. In this way, an urban area is divided into a collection of smaller landscapes, larger than individual households and smaller than census tracts, that are distinct in how they function. The study shows that characterizing the urban landscape in this way can distinguish between neighborhoods with different travel behaviors. Extensions of the schema can be used to monitor and manage urban systems towards sustainability, targeting spatial planning strategies to the micro-geographies where they would be most relevant.

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Modeling the Effect of Land Use on Activity Spaces

Cited by 29 publications

References 26 publications

A Spatial and Temporal Comparative Analysis of the Effects of Land-Use Clusters on Activity Spaces in Three Quebec Cities

A Spatial and Temporal Comparative Analysis of the Effects of Land-Use Clusters on Activity Spaces in Three Quebec Cities

A statistical framework for measuring the temporal stability of human mobility patterns

Characterizing and measuring urban landscapes for sustainability

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