Featured Graphic. OD Maps for Showing Changes in Irish Female Migration between 1851 and 1911

Slingsby, Aidan; Kelly, M.; Dykes, Jason

doi:10.1068/a140112g

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…Origin‐destination (OD) data describe the movements from origins to destinations. Many OD studies are about movements of humans, e.g., migration [Rob67, PXY ∗ 05, Tob87, SKDW12, SKD14, NB13, AS14, BBBL11, VBS11] and passenger transport [LWLY15, YWZ ∗ 19, LCvL17, LBR ∗ 16, AAFW17]. Other types of movements have also been studied, e.g., the export of goods [Rob67], the movement of animals [SPR ∗ 18], and the spreading of diseases [Guo07].…”

Section: Origin‐destination Data Visualization (Oddv)mentioning

confidence: 99%

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Design Space of Origin‐Destination Data Visualization

Tennekes

Chen

2021

Computer Graphics Forum

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Visualization is an essential tool for observing and analyzing origin‐destination (OD) data, which encodes flows between geographic locations, e.g., in applications concerning commuting, migration, and transport of goods. However, depicting OD data often encounter issues of cluttering and occlusion. To address these issues, many visual designs feature data abstraction and visual abstraction, such as node aggregation and edge bundling, resulting in information loss. The recent theoretical and empirical developments in visualization have substantiated the merits of such abstraction, while confirming that viewers' knowledge can alleviate the negative impact due to information loss. It is thus desirable to map out different ways of losing and adding information in origin‐destination data visualization (ODDV). We therefore formulate a new design space of ODDV based on the categorization of informative operations on OD data in data abstraction and visual abstraction. We apply this design space to existing ODDV methods, outline strategies for exploring the design space, and suggest ideas for further exploration.

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Section: Origin‐destination Data Visualization (Oddv)mentioning

confidence: 99%

“…Spatial clustering techniques have been used to order the rows (origins) and columns (destinations) [Guo07, LCvL17]. The OD map [WDS10, SKDW12, SKD14] provides matrix representations with some geospatial information by relating to the position of matrix cells coarsely, and often hierarchically, to the corresponding geographical locations.…”

Section: Related Workmentioning

confidence: 99%

Design Space of Origin‐Destination Data Visualization

Tennekes

Chen

2021

Computer Graphics Forum

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“…These tiles are then arranged in such a way as to reflect important characteristics of the spatial dimension, often using whitespace to capture salient local features. Grid maps are used to visualize geospatial data by [1,2,7,10,26,28,29,44], discussed by mapping enthusiasts and professionals [12, 30-36, 48, 49], and applied in the academic literature [15,40,42,43,50,52]. The tiles of a grid map allow for visualizing data in a small-multiples style as popularized by Tufte [45].…”

Section: Introductionmentioning

confidence: 99%

A Simple Pipeline for Coherent Grid Maps

Meulemans

Sondag

Speckmann

2021

IEEE Trans. Visual. Comput. Graphics

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“…These data are presented in Figures 3 and 4 as Origin-Destination flow maps (OD maps; Slingsby et al., 2014; Wood et al., 2010). We attempt a graphical explanation of the OD map layout in Figure 2.…”

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confidence: 99%

Characterising labour market self-containment in London with geographically arranged small multiples

Beecham

Slingsby

2019

Environ Plan A

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We present a collection of small multiple graphics that support analysis and understanding of the geography of labour-market self-containment across London’s 33 boroughs. Ratios describing supply-side self-containment, the extent to which working residents access jobs locally, and demand-side self-containment, the extent to which local jobs are filled by local resident workers, are first calculated for professional and non-professional occupations and encoded directly through geographically-arranged bar charts. The full distribution of workers into-and out-of- boroughs that underpins these ratios is then revealed via Origin-Destination flows maps (OD maps) – sets of geographically-arranged choropleths. In order to make relative and absolute comparison of borough-to-borough frequencies between occupation types, these OD maps are coloured according to signed chi-square residuals: for every borough-to-borough pair, we compare the observed number of flows to access professional versus non-professional jobs against the number that would be expected given the distribution of those jobs across London boroughs. Our geographically-arranged small multiples demonstrate potential for spatial analysis: a rich, multivariate structure is depicted that reflects London’s economic geography and that would be difficult to expose using non-visual means.

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Featured Graphic. OD Maps for Showing Changes in Irish Female Migration between 1851 and 1911

Cited by 4 publications

References 2 publications

Design Space of Origin‐Destination Data Visualization

Design Space of Origin‐Destination Data Visualization

A Simple Pipeline for Coherent Grid Maps

Characterising labour market self-containment in London with geographically arranged small multiples

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