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vignettes/paper.Rmd

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@@ -141,7 +141,7 @@ In order to address these shortcomings of zoning systems based on administrative
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The focus of much preceding zoning research on BSU partitioning can be explained by the fact that much geographic data available to academics comes in 'pre-packaged' small areas and because creating zones from nothing is a harder problem.
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The statement that "existence of individual or non-spatially aggregated data is rare in geography", used by @openshaw_optimal_1977 to justify the BSU grouping approach, may have been true in the 1970s when it was written.
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Today disaggregated geographic datasets are common.
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Open datasets exist on phenomena including as car crashes, shop locations, species identification data and dozens of other phenomena that can be understood as 'point pattern processes'.
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Open datasets exist on phenomena including car crashes, shop locations, species identification data and dozens of other phenomena that can be understood as 'point pattern processes'.
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And with advances in computer hardware and software, the 'starting from scratch' approach to zoning systems is more feasible.
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A number of approaches have tackled the question of how to best divide up geographical space for analysis and visualisation purposes, with a variety of applications.
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## Describing location
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A potential application of the zoning system is to indicate approximate locations with reference to a known central point or area, e.g. to describe a segment of a city.
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ClockBoard zones offer a level of intermediate-to-low accuracy in between the simple use of quadrants to identify parts of a city verbally on one hand, and more sophisticated ways of communicating location to the nearest few metres on the other.
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ClockBoard zones offer a level of intermediate-to-low accuracy in between the simple use of quadrants to identify parts of a city verbally on one hand, and more sophisticated ways of communicating location to the nearest few meters on the other.
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Dividing cities into quadrants and referring to them with names such as 'north', 'northeast', etc. is common in everyday speech and academic writing: a paper on the impact of open spaces on house prices stated that traffic noise was expected to have a negative impact on house prices in "south-east, north-east, and north Portland", with reference to an accompanying map, for example [@bolitzer_impact_2000].
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Location services such as 'what3words' and open source implementations such as 'whatfreewords' and '[goehashphrase](https://www.qalocate.com/solutions/geohashphrase/)' take the concept of converting coordinates into memorable words/phrases a step further, offering accuracy measured in meters rather than kilometers in situations where coordinates may not be appropriate or possible [@raposo_virtual_2019].
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While official boundaries can vary greatly in size and shape, based on sometimes arbitrary factors such as historic boundaries (as shown in Figure \@ref(fig:intercity), top), ClockBoard zones are always the same size, shape and orientation (as shown in Figure \@ref(fig:intercity), bottom).
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Using the system can provide a basis of evidence-based discussion of geographically aggregated results representing urban phenomena.
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The example demonstrates this with reference to a policy-relevant example: the number of people killed and seriously injured while cycling in major UK cities.
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Addressing issues associated with reporting only number of casualties per unit area, a practice that can miss dangerous places which have a high casualty rate per unit time or distance cycled [@mindell_exposure-based_2012], we show data on the number of people killed and seriously injured while cycling per billion kilometres based on estimates from the Propensity to Cycle Tool [@lovelace_propensity_2017].
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Addressing issues associated with reporting only number of casualties per unit area, a practice that can miss dangerous places which have a high casualty rate per unit time or distance cycled [@mindell_exposure-based_2012], we show data on the number of people killed and seriously injured while cycling per billion kilometers based on estimates from the Propensity to Cycle Tool [@lovelace_propensity_2017].
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```{r intercity, fig.width=7, message=FALSE, warning=FALSE, fig.cap="Comparison of administrative zones (top) and zones in the ClockBoard zoning system (bottom) to support inter-city comparison of policy-relevant data, on road traffic casualties. The maps show the spatial distribution of cycling casualties per billion km cycled, a measure that requires spatial data aggregation for meaningful results.", out.width="85%", fig.show='hold'}
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# download preprocessed data (processing script /data-raw/crashes.R)

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