Transport shaping space: differential collapse in time–space

Knowles, Richard

doi:10.1016/j.jtrangeo.2006.07.001

Cited by 180 publications

(122 citation statements)

References 48 publications

(53 reference statements)

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“…Individuals who possess different socioeconomic attributes may have different levels of popularity and activity in forming their networks. Physical environment, infrastructure, and technology may also shape social networks (Fotheringham 1981, Ellegård and Vilhelmson 2004, Knowles 2006, Ilahiane and Sherry 2012, Matous et al 2013. Previous studies have suggested that newly available informationcommunication technologies may facilitate agricultural information-sharing ties, particularly in less-developed regions (Leeuwis andVan den Ban 2004, Donner 2008).…”

Section: The Life Of a Networkmentioning

confidence: 99%

Exploring dynamic mechanisms of learning networks for resource conservation

Matouš¹,

Todo²

2015

E&S

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ABSTRACT. The importance of networks for social-ecological processes has been recognized in the literature; however, existing studies have not sufficiently addressed the dynamic nature of networks. Using data on the social learning networks of 265 farmers in Ethiopia for 2011 and 2012 and stochastic actor-oriented modeling, we explain the mechanisms of network evolution and soil conservation. The farmers' preferences for information exchange within the same social groups support the creation of interactive, clustered, nonhierarchical structures within the evolving learning networks, which contributed to the diffusion of the practice of composting. The introduced methods can be applied to determine whether and how social networks can be used to facilitate environmental interventions in various contexts.

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Section: The Life Of a Networkmentioning

confidence: 99%

Exploring dynamic mechanisms of learning networks for resource conservation

Matouš¹,

Todo²

2015

E&S

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“…This can cause a gap between cost distance and geographical distance (due to planes flying longer routes than great circle ones), but our purpose was to use a standard database (great circle distance between city-pairs does not change but flight distance is different in each case due to the aforementioned factors), so considering the literature we assumed that this gap is incorporated into the ticket price and inter-city great circle distance is an appropriate measure of city distance. Doganis (2002) found that the relationship of air transport cost to distance is curvilinear and not a straight line, as transport costs normally decrease per unit distance traveled (Knowles, 2006;Taaffe, Gauthier, & O'Kelly, 1996). By the determination of the price per distance value we had to take into consideration that fares increase with distance but not in a linear way, so this value -similarly to the flight distance -may differ in each city-pair (e.g.…”

Section: Calculation Of Cost Distancementioning

confidence: 99%

Mapping cost distance using air traffic data

et al. 2015

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Recent trends in transport and communication infrastructures have had a profound impact on the spatial organization of the world city network, which have long been of interest to geographers. Considering the former issue, our study is based on previous works on air transport geography and world city network studies. We introduce a new method to map the gap between geographical distance and cost distance by using air traffic data. In this paper, we created an international database for a large number of world cities and developed a way to map cost distance using conventional and Geographic Information System-based mapping techniques. The main result of this work is a set of maps showing the cost distances of world cities, which can be used as a significant source of information by world city network analysis. ARTICLE HISTORY

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“…Land use-transport modelers recognize that transport investments shape -to some extent -land use patterns (Knowles, 2006). Wang and Kockelman's DSOP study (2009b) analyzed the impact of distances to highways on grid cells' land development intensities.…”

Section: Introductionmentioning

confidence: 99%

The dynamic spatial multinomial probit model: analysis of land use change using parcel-level data

Wang

Kockelman

Lemp

2012

Journal of Transport Geography

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Many transportation-related behaviors involve multinomial discrete response in a temporal and spatial context. These include quality of paved roadway sections over time, evolution of land use at the parcel level, vehicle purchases by socially networked households, and mode choices by individuals residing across adjacent homes or neighborhoods. Such responses depend on various influential factors, and can have temporal and spatial dependence or autocorrelation. In many cases, dynamic spatial-model specifications based on maximum fitness, profit or utility may be most appropriate.This study develops a Dynamic Spatial Multinomial Probit (DSMNP) model by pivoting off the ordinary MNP model while incorporating spatial and temporal dependencies. The paper first explains how the model reflects the behaviors at play, and then describes estimation using Bayesian methods. Simulated data sets containing both generic and alternative-specific explanatory variables are used to validate the model's performance (and that of its associated code). Estimation efficiency issues and identification issues are discussed. The model is then applied to analyze parcel-level land use changes in Austin, Texas. It is found that better accessibility boosts the potential of residential development, with a statistically significant parameter. In the case of non-residential development, the effect is statistically insignificant. The effects of job and population density, neighborhood income and soil slope are also explored, and found to exert variable effects across space. It is also found that land development tends to cluster when existing development intensity in a neighborhood is low.2

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Transport shaping space: differential collapse in time–space

Cited by 180 publications

References 48 publications

Exploring dynamic mechanisms of learning networks for resource conservation

Exploring dynamic mechanisms of learning networks for resource conservation

Mapping cost distance using air traffic data

The dynamic spatial multinomial probit model: analysis of land use change using parcel-level data

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