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book.bib
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@Book{xie2015,
title = {Dynamic Documents with {R} and knitr},
author = {Yihui Xie},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2015},
edition = {2nd},
note = {ISBN 978-1498716963},
url = {http://yihui.name/knitr/},
}
@article{moreno-monroy_public_2017,
title = {Public Transport and School Location Impacts on Educational Inequalities: {{Insights}} from {{S\~ao Paulo}}},
volume = {67},
issn = {0966-6923},
shorttitle = {Public Transport and School Location Impacts on Educational Inequalities},
doi = {10.1016/j.jtrangeo.2017.08.012},
abstract = {In many large Latin American urban areas such as the S\~ao Paulo Metropolitan Region (SPMR), growing social and economic inequalities are embedded through high spatial inequality in the provision of state schools and affordable public transport to these schools. This paper sheds light on the transport-education inequality nexus with reference to school accessibility by public transport in the SPMR. To assess school accessibility, we develop an accessibility index which combines information on the spatial distribution of adolescents, the location of existing schools, and the public transport provision serving the school catchment area into a single measure. The index is used to measure school accessibility locally across 633 areas within the SPMR. We use the index to simulate the impact of a policy aiming at increasing the centralisation of public secondary education provision, and find that it negatively affects public transport accessibility for students with the lowest levels of accessibility. These results illustrate how existing inequalities can be amplified by variable accessibility to schools across income groups and geographical space. The research suggests that educational inequality impacts of school agglomeration policies should be considered before centralisation takes place.},
journal = {Journal of Transport Geography},
author = {{Moreno-Monroy}, Ana I. and Lovelace, Robin and Ramos, Frederico R.},
month = sep,
year = {2017},
keywords = {Schools,Accessibility,Public transport,Latin America,Inequality},
file = {/home/robin/Zotero/storage/FCEI6MKH/S0966692316303453.html}
}
@article{lovelace_propensity_2017,
title = {The {{Propensity}} to {{Cycle Tool}}: {{An}} Open Source Online System for Sustainable Transport Planning},
volume = {10},
copyright = {Copyright (c) 2016 Robin Lovelace, Anna Goodman, Rachel Aldred, Nikolai Berkoff, Ali Abbas, James Woodcock},
issn = {1938-7849},
shorttitle = {The {{Propensity}} to {{Cycle Tool}}},
doi = {10.5198/jtlu.2016.862},
abstract = {Getting people cycling is an increasingly common objective in transport planning institutions worldwide. A growing evidence base indicates that high quality infrastructure can boost local cycling rates. Yet for infrastructure and other cycling measures to be effective, it is important to intervene in the right places, such as along `desire lines' of high latent demand. This creates the need for tools and methods to help answer the question `where to build?'. Following a brief review of the policy and research context related to this question, this paper describes the design, features and potential applications of such a tool. The Propensity to Cycle Tool (PCT) is an online, interactive planning support system that was initially developed to explore and map cycling potential across England (see www.pct.bike). Based on origin-destination data it models cycling levels at area, desire line, route and route network levels, for current levels of cycling, and for scenario-based `cycling futures.' Four scenarios are presented, including `Go Dutch' and `Ebikes,' which explore what would happen if English people had the same propensity to cycle as Dutch people and the potential impact of electric cycles on cycling uptake. The cost effectiveness of investment depends not only on the number of additional trips cycled, but on wider impacts such as health and carbon benefits. The PCT reports these at area, desire line, and route level for each scenario. The PCT is open source, facilitating the creation of scenarios and deployment in new contexts. We conclude that the PCT illustrates the potential of online tools to inform transport decisions and raises the wider issue of how models should be used in transport planning.},
language = {en},
number = {1},
journal = {Journal of Transport and Land Use},
author = {Lovelace, Robin and Goodman, Anna and Aldred, Rachel and Berkoff, Nikolai and Abbas, Ali and Woodcock, James},
month = jan,
year = {2017},
keywords = {Planning,Cycling,modelling,Participatory},
file = {/home/robin/Zotero/storage/P3IKZ3IC/862-4512-1-PB.pdf}
}
@book{lovelace_geocomputation_2019,
title = {Geocomputation with {{R}}},
isbn = {1-138-30451-4},
abstract = {Book on geographic data with R.},
publisher = {{CRC Press}},
author = {Lovelace, Robin and Nowosad, Jakub and Muenchow, Jannes},
year = {2019}
}
@article{goodman_scenarios_2019,
title = {Scenarios of Cycling to School in {{England}}, and Associated Health and Carbon Impacts: {{Application}} of the `{{Propensity}} to {{Cycle Tool}}'},
volume = {12},
issn = {2214-1405},
shorttitle = {Scenarios of Cycling to School in {{England}}, and Associated Health and Carbon Impacts},
doi = {10.1016/j.jth.2019.01.008},
abstract = {Background
The Propensity to Cycle Tool (PCT) is a freely available, interactive tool help prioritise cycling initially launched in England in 2017 and based on adult commuting data. This paper applies the method to travel to school data, and assesses health and carbon benefits based on nationwide scenarios of cycling uptake.
Methods
The 2011 National School Census provides origin-destination data for all state-funded schools in England (N = 7,442,532 children aged 2\textendash{}18 in 21,443 schools). Using this dataset, we modelled propensity to cycle as a function of route distance and hilliness between home and school. We generated scenarios, including `Go Dutch' \textendash{} in which English children were as likely to cycle as Dutch children, accounting for trip distance and hilliness. We estimated changes in the level of cycling, walking, and driving, and associated impacts on physical activity and carbon emissions.
Results
In 2011, 1.8\% of children cycled to school (1.0\% in primary school, 2.7\% in secondary school). If Dutch levels of cycling were reached, under the Go Dutch scenario, this would rise to 41.0\%, a 22-fold increase. This is larger than the 6-fold increase in Go Dutch for adult commuting. This would increase total physical activity among pupils by 57\%, and reduce transport-related carbon emissions by 81 kilotonnes/year. These impacts would be substantially larger in secondary schools than primary schools (a 96\% vs. 9\% increase in physical activity, respectively).
Conclusion
Cycling to school is uncommon in England compared with other Northern European countries. Trip distances and hilliness alone cannot explain the difference, suggesting substantial unmet potential. We show that policies resulting in substantial uptake of cycling to school would have important health and environmental benefits. At the level of road networks, the results can inform local investment in safe routes to school to help realise these potential benefits.},
journal = {Journal of Transport \& Health},
author = {Goodman, Anna and Rojas, Ilan Fridman and Woodcock, James and Aldred, Rachel and Berkoff, Nikolai and Morgan, Malcolm and Abbas, Ali and Lovelace, Robin},
month = mar,
year = {2019},
keywords = {School,Carbon emissions,Active travel,Physical activity,Cycling,Modelling},
pages = {263-278},
file = {/home/robin/Zotero/storage/JR56F4C4/Goodman et al. - 2019 - Scenarios of cycling to school in England, and ass.pdf;/home/robin/Zotero/storage/EQ8SHGTZ/S2214140518301257.html}
}
@article{lovelace_stats19_2019,
title = {Stats19: {{A}} Package for Working with Open Road Crash Data},
doi = {10.21105/joss.01181},
journal = {Journal of Open Source Software},
author = {Lovelace, Robin and Morgan, Malcolm and Hama, Layik and Padgham, Mark},
year = {2019}
}