Mapping the changing residential geography of White British secondary school children in England using visually balanced cartograms and hexograms

Harris, Richard J; Charlton, Martin; Brunsdon, Chris

doi:10.1080/17445647.2018.1478753

Cited by 8 publications

(8 citation statements)

References 15 publications

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“…2019). Developments in cartography and the presentation of spatial data (Robinson et al, 2017; Cheshire and Uberti, 2017), much of it pioneered by Tobler (see Unwin, 2019), include increasing use of cartograms (Tobler, 2004; Dorling and Thomas, 2016; Nusrat and Kobourov, 2016; Harris et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

Quantitative methods II: How we moved on – Decades of change in philosophy, focus and methods

Johnston

Harris

Jones

et al. 2019

Progress in Human Geography

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The first of these three reports reprised human geography’s theoretical and quantitative revolutions’ origins, covering the philosophy, focus and methods that dominated their early years. Over the subsequent decades the nature of work categorised as quantitative human geography changed very considerably – in philosophy, focus and methods. This second report summarises those changes, highlighting the main features of the extensive volume of work published over the last five decades, as a prelude to the final report that will focus on the contemporary nature of quantitative human geography and its likely futures.

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Section: Methodsmentioning

confidence: 99%

Quantitative methods II: How we moved on – Decades of change in philosophy, focus and methods

Johnston

Harris

Jones

et al. 2019

Progress in Human Geography

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“…Maps visualize the spatial distribution for OA, LSOA and MSOA respectively. We deploy hexograms (Harris et al, 2018) instead of mapping the original boundaries. These have been shown to maintain the spatial patterning (e.g.…”

Section: Analytical Strategymentioning

confidence: 99%

“…Offender residence concentration counts in 2006/07 using hexograms(Harris et al, 2018) by spatial scale. Five low to high breaks computed using the Fisher-Jenks algorithm as perSchnell et al (2017).…”

mentioning

confidence: 99%

An examination of variability in offender residences across different spatial scales. A case study in Birmingham.

Langton¹,

Steenbeek²,

Adepeju³

2020

Preprint

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Objectives: This paper disentangles the degree of concentration and variance in offender residences across different levels of spatial aggregation. Three nested units are analysed simultaneously (and longitudinally) to explore the impact of using different spatial scales, opening prospect for a comparison with existing findings from crime concentration literature. Methods: Data is utilized from West Midlands Police Force, containing the locations of residence for all known offenders in Birmingham between the years 2006 and 2016. Resident locations are aggregated to 3223 Output Areas (OA), nested within 639 Lower Super Output Areas (LSOA), further nested within 132 Middle Super Output Areas (MSOA). Descriptive and model-based statistics are deployed to replicate a recent study for crime events (Steenbeek & Weisburd, 2016). Results: In contrast to the crime concentration literature, the results indicate that most variance (~50%) in offender residence concentrations is attributable to the largest spatial scale (MSOA level). Output Areas, as the most fine-grained unit, capture approximately 39% of the variance, although this proportion increases during the study period at the expense of MSOA. Conclusions: Findings suggest that the relationship between variance and scale when studying offender residences is not as clear-cut as it is for crimes. Larger units hold some merit (empirically and theoretically), but this is time-dependent, opening up discussions on the role of urban development in determining the appropriateness of spatial scale.

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“…This approach has also been extended to include a 'hexogram', whereby an iterative binning algorithm assigns the centroid of polygons from the balanced cartogram to tessellated hexagons, each representing the original polygons. In doing so, the data are said to maintain spatial accuracy whilst also being uniform in shape and size (Harris et al, 2018a(Harris et al, , 2018b. Comparable alternatives to this approach are tile maps which use a distance-based procedure (e.g.…”

Section: Introductionmentioning

confidence: 99%

Cartograms, hexograms and regular grids: Minimising misrepresentation in spatial data visualisations

Langton

Solymosi

2019

Environment and Planning B: Urban Analytics and City Science

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The visualisation of spatial information is a powerful tool for researchers in urban analytics seeking to convey their findings to the wider research community and the public in an accessible way. Yet, even the most well-intentioned cartographer may introduce misrepresentation by mapping irregularly shaped and sized areas. This paper explores the extent to which different methods of visualising area-based data can remedy (or exacerbate) this misrepresentation by presenting results from a crowdsourced survey. Data from the 2016 European Union referendum at Local Authority level in England are visualised using four alternative methods (balanced cartogram, hexogram, hexagonal grid, square grid) and compared to a traditional choropleth map, in terms of people’s understanding of the authors’ intended message, through a crowdsourced survey questionnaire. Results indicate that mapping out original boundaries can introduce misrepresentation, which can be mitigated by using balanced cartograms and hexograms to improve the accuracy of visualisations.

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Mapping the changing residential geography of White British secondary school children in England using visually balanced cartograms and hexograms

Cited by 8 publications

References 15 publications

Quantitative methods II: How we moved on – Decades of change in philosophy, focus and methods

Quantitative methods II: How we moved on – Decades of change in philosophy, focus and methods

An examination of variability in offender residences across different spatial scales. A case study in Birmingham.

Cartograms, hexograms and regular grids: Minimising misrepresentation in spatial data visualisations

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