Introducing spatial microsimulation with R: a practical

This practical teaches the basic theory and practice of `spatial microsimulation' using the popular free software package R. The term microsimulation means different things in different disciplines, so it is important to be clear at the outset what we will and will not be covering. We will be learning how to create spatial microdata, the basis of all spatial microsimulation models, using iterative proportional fitting (IPF). IPF is an efficient method for allocating in- dividuals from a non-spatial dataset to geographical zones, analogous to the `Furness method' in transport modelling, but with more constraints. There are other ways of generating spatial microdata but, as far as the author is aware,1 this is the most effective and flexible for many applications. An alternative approach using the open source `Flexible Modelling Framework' program is described in detail, with worked examples, by Harland (2013). We will not be learning `dynamic spatial microsimulation' (Ballas et al., 2005): once the spatial microdata have been generated and integerised, it is up to the user how they are used, be it in an agent based model or as a basis for estimates of income distributions at the local level or whatever. We thus define spatial microsimulation narrowly in this tutorial as the process of generating spatial microdata (more on this below). The term can also be used to describe a wider approach that harnesses individual-level data allocated to zones for investigating phenomena that vary over space and between individuals such as income inequality or energy overconsumption. In both cases, the generation of spatial microdata is the critical element of the modelling process so the skills learned in this tutorial will provide a firm foundation for further work

Multivariate hierarchical analysis of car crashes data considering a spatial network lattice

Road traffic casualties represent a hidden global epidemic, demanding evidence-based interventions. This paper demonstrates a network lattice approach for identifying road segments of particular concern, based on a case study of a major city (Leeds, UK), in which 5,862 crashes of different severities were recorded over an eight-year period (2011-2018). We consider a family of Bayesian hierarchical models that include spatially structured and unstructured random effects, to capture the dependencies between the severity levels. Results highlight roads that are more prone to collisions, relative to estimated traffic volumes, in the northwest and south of city-centre. We analyse the Modifiable Areal Unit Problem (MAUP), proposing a novel procedure to investigate the presence of MAUP on a network lattice. We conclude that our methods enable a reliable estimation of road safety levels to help identify "hotspots" on the road network and to inform effective local interventions.Comment: 23 pages, 5 tables, 8 figure

Exploring the effect of local transport policies on the adoption of low emission vehicles: Evidence from the London Congestion Charge and Hybrid Electric Vehicles

The London Congestion Charge (LCC) is a transport policy with a precise spatial footprint. As such, its impact on the transport system can be expected to vary over space, providing an opportunity to explore the geographical reach of local transport interventions. This paper assesses whether the exemption of Hybrid Electric Vehicles (HEVs) from the LCC affected the registration rate of these vehicles in Greater London and the surrounding areas. The analysis uses official data on the number of HEVs registered across the local authorities of the United Kingdom. This dataset is assessed using [1] exploratory spatial analysis to determine the degree of spatial variation in HEV registrations, [2] area classifications to consider if HEV registrations diminish as nearness to the LCC recedes, and [3] spatial regression models to evaluate the association between distance to the LCC and HEV registrations, controlling for other area characteristics (i.e. socioeconomic, household, and transport system variables). The results clearly show that nearness to the LCC is positively associated with HEV registrations, implying that this form of transport policy is effective at promoting the adoption of low emission vehicles

Introduction to visualising spatial data in R

This tutorial is an introduction to spatial data in R and map making with R's `base' graphics and the popular graphics package ggplot2. It assumes no prior knowledge of spatial data analysis but prior understanding of the R command line would be beneficial. For people new to R, we recommend working through an `Introduction to R' type tutorial, such as "A (very) short introduction to R" (Torfs and Brauer, 2012) or the more geographically inclined "Short introduction to R" (Harris, 2012). Building on such background material, the following set of exercises is concerned with specific functions for spatial data and visualisation. It is divided into five parts: *Introduction, which provides a guide to R's syntax and preparing for the tutorial *Spatial data in R, which describes basic spatial functions in R *Manipulating spatial data, which includes changing projection, clipping and spatial joins *Map making with ggplot2, a recent graphics package for producing beautiful maps quickly *Taking spatial analysis in R further, a compilation of resources for furthering your skills An up-to-date version of this tutorial is maintained at https://github.com/Robinlovelace/Creating-maps-in-R and the entire tutorial, including the input data can be downloaded as a zip file, as described below. The entire tutorialwas written in RMarkdown, which allows R code to run as the document compiles. Thus all the examples are entirely reproducible. Suggested improvements welcome - please fork, improve and push this document to its original home to ensure its longevity. The tutorial was developed for a series of Short Courses put on by the National Centre for Research Methods, via the TALISMAN node (see geotalisman.org)

Public transport and school location impacts on educational inequalities: Insights from São Paulo

In many large Latin American urban areas such as the São 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

A Path Toward the Use of Trail Users’ Tweets to Assess Effectiveness of the Environmental Stewardship Scheme: An Exploratory Analysis of the Pennine Way National Trail

Large and unofficial data sets, for instance those gathered from social media, are increasingly being used in geographical research and explored as decision support tools for policy development. Social media data have the potential to provide new insight into phenomena about which there is little information from conventional sources. Within this context, this paper explores the potential of social media data to evaluate the aesthetic management of landscape. Specifically, this project utilises the perceptions of visitors to the Pennine Way National Trail, which passes through land managed under the Environmental Stewardship Scheme (ESS). The method analyses sentiment in trail users’ public Twitter messages (tweets) with the aim of assessing the extent to which the ESS maintains landscape character within the trail corridor. The method demonstrates the importance of filtering social media data to convert it into useful information. After filtering, the results are based on 161 messages directly related to the trail. Although small, this sample illustrates the potential for social media to be used as a cheap and increasingly abundant source of information. We suggest that social media data in this context should be seen as a resource that can complement, rather than replace, conventional data sources such as questionnaires and interviews. Furthermore, we provide guidance on how social media could be effectively used by conservation bodies, such as Natural England, which are charged with the management of areas of environmental value worldwide

Open access transport models: A leverage point in sustainable transport planning

A large and growing body of evidence suggests fundamental changes are needed in transport systems, to tackle issues such as air pollution, physical inactivity and climate change. Transport models can play a major role in tackling these issues through the transport planning process, but they have historically been focussed on motorised modes (especially cars) and available only to professional transport planners working within the existing paradigm. Building on the principles of open access software, first developed in the context of geographic information systems, this paper develops and discusses the concept of open access transport models, which we define as models that are both developed using open source software and are available to be used by the public without the need for specialist training or the purchase of software licences. We explore the future potential of open access transport models to support the transition away from fossil fuels in the transport sector. We do this with reference to the literature on the use of tools in the planning process, and by exploring an example that is already in use: the ‘Propensity to Cycle Tool’. We conclude that open access transport models can be a leverage point in the planning process due to their ability to provide robust, transparent and actionable evidence that is available to a range of stakeholders, not just professional transport planners. Open access transport models represent a disruptive technology deserving further research and development, by planners, researchers and citizen scientists, including open source software developers and advocacy groups but, in order to fulfil their potential, they will require both financial and policy support from government bodies

The energy costs of commuting: a spatial microsimulation approach

Commuting is a daily ritual for a large proportion of the world's population. It is important materially, consuming large amounts of time, money and natural resources. As with many routine activities travel to work is often taken for granted but its energy consumption is of particular interest due to its heavy reliance on fossil fuels and the inflexibility of the demand for commuting. This understudied area of knowledge, the energy costs of travel to work, forms the basis of the thesis. There is much research into commuting and transport energy use as separate fields, but they have rarely been combined in the same analysis, let alone at high levels of geographical resolution. The well-established field of spatial microsimulation offers tools for investigating commuting patterns in detail at local and individual levels, with major potential benefits for transport planning. For the first time this method is deployed to investigate variability in commuter energy use both between and within small administrative zones. The maps of commuter energy use presented in this thesis illustrate this variability at national, regional and local levels. Supporting previous research, the results suggest that a range of geographical factors influence energy use for travel. This has important policy implications: when high transport energy use in commuting is due to lack of jobs in the vicinity, for example, modal shift (e.g.~from cars to bicycles) on its own has a limited potential to reduce energy costs. Such insights are quantified using existing aggregate data. The main methodological contribution of this work, however, is to add individual-level factors to the analysis - creating the potential for policy makers to also assess the distributional impacts of their interventions and target specific types of commuters having high transport energy costs, rather than treat areas as homogeneous blocks. This potential is demonstrated with a case study of South Yorkshire, where commuting energy use is cross-tabulated by socio-economic variables and disaggregated over geographical space. The areas where commuting energy use is less evenly distributed across the population, for example in urban centres, are likely to benefit most from policies that target the specific groups. Areas where commuter energy use is more even, such as Stocksbridge (in Northwest Sheffield), will benefit from more universal policies. The thesis contributes to human knowledge new information about the energy costs of commuting, its variability at various levels and insight into the implications. New methods of generating and analysing individual-level data for the analysis of commuter energy use have also been developed. These are reproducible (see the GitHub repository https://github.com/Robinlovelace/thesis-reproducible for example code and data) and will be of interest to researchers and policy makers investigating the energy security, resource efficiency and potential welfare impacts of interventions in personal travel systems